[1]Ekin Yagis

These authors contributed equally to this work.

These authors contributed equally to this work.

These authors contributed equally to this work.

These authors contributed equally to this work.

These authors contributed equally to this work.

These authors contributed equally to this work.

These authors contributed equally to this work.

[1]Department of Mechanical Engineering, University College London, London, UK

2]European Synchrotron Radiation Facility, Grenoble, France

3]Department of Intelligent Systems Engineering, Luddy School of Informatics,
Computing, and Engineering, Indiana University, Bloomington, USA

4]Centre for Medical Image Computing, University College London, London UK

5]Laboratoire d’Anatomie Des Alpes Françaises, Grenoble, France

6]Institute of Pathology, Hannover Medical School, Carl-Neuberg-Straße 1,
30625, Hannover, Germany.

6]Institute of Functional and Clinical Anatomy, University Medical Center of
the Johannes Gutenberg-University Mainz, Mainz, Germany.

8]Member of the German Center for Lung Research (DZL), Biomedical Research in
Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany.

# Deep Learning for Vascular Segmentation and Applications in Phase Contrast
Tomography Imaging

<EMAIL_ADDRESS>Shahab Aslani  Yashvardhan Jain  Yang Zhou  Shahrokh Rahmani
Joseph Brunet  Alexandre Bellier  Christopher Werlein  Maximilian Ackermann
Danny Jonigk  Paul Tafforeau  Peter D Lee  Claire Walsh  *  [  [  [  [  [  [
[

###### Abstract

Automated blood vessel segmentation is critical for biomedical image analysis,
as vessel morphology changes are associated with numerous pathologies. Still,
precise segmentation is difficult due to the complexity of vascular
structures, anatomical variations across patients, the scarcity of annotated
public datasets, and the quality of images.

We present a thorough literature review, highlighting the state of machine
learning techniques across diverse organs. Our goal is to provide a foundation
on the topic and identify a robust baseline model for application to vascular
segmentation in a new imaging modality, Hierarchical Phase-Contrast Tomography
(HiP-CT). Introduced in 2020 at the European Synchrotron Radiation Facility,
HiP-CT enables 3D imaging of complete organs at an unprecedented resolution of
ca. 20µm/voxel, with the capability for localized zooms in selected regions
down to 1µm/voxel without sectioning. Through our review and application of
baseline models to HiP-CT data we aim to provide a future benchmark for models
applied to HiP-CT data. We have created a training dataset with double
annotator-validated vascular data from three kidneys imaged with HiP-CT in the
context of the Human Organ Atlas Project. Finally, utilising the nnU-Net
model, we conduct experiments to assess the models’ performance on both
familiar and unseen samples, employing vessel-specific metrics.

Our results show that while segmentations yielded reasonably high scores—such
as clDice values ranging from 0.82 to 0.88, certain errors persisted.
Specifically, large vessels that collapsed due to the lack of hydro-static
pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover,
decreased connectivity in finer vessels and higher segmentation errors at
vessel boundaries were observed. Such errors, particularly in significant
vessels, obstruct the understanding of the structures by interrupting vascular
tree connectivity. Through our review and outputs, we aim to set a benchmark
for subsequent model evaluations using various modalities, especially with the
HiP-CT imaging database.

###### keywords:

Deep learning, X-ray tomography, semantic segmentation, 3D vascular
segmentation

##  1 Introduction

The vascular, or circulatory system consists of the network of blood vessels
responsible for circulating blood, delivering oxygen, and facilitating the
removal of waste from tissues and organs pugsley2000vascular . Abnormalities
in the structure or function of vascular networks can lead to, or be
indicative of, a myriad of pathologies ranging from tumor growth and
metastasis to strokes and cardiovascular disorders lee2019organs . Hence,
accurate segmentation of vasculature and quantitative evaluation of its
morphology are widely applied to better understand these pathophysiological
processes potente2017link ; sweeney2018role ; moccia2018blood ;
rust2020practical .

Annotation of images by experts has been regarded as the segmentation gold
standard, but it is time-consuming and requires specialised knowledge
zhao2019segmentation . The success of convolutional neural networks (CNN) in
classification leads to researchers attempting to employ deep learning
technologies for image segmentation goni2022brain . Consequently, a growing
number of automated and semi-automatic vessel segmentation methods have been
developed over the last years for the diagnosis of diseases associated with
the vascular system fraz2012blood ; zhao2019segmentation ; moccia2018blood .

In order to be clinically useful, segmentation algorithms need to be capable
of handling a wide range of anatomical/sample variability and to be able to
function on different types of imaging modalities and qualities. Advances in
3D imaging, image analysis, and image-based modeling have resulted in complex
frameworks for gathering as well as simulating structural and functional
physiological data sweeney2018role . Magnetic resonance imaging (MRI),
positron emission tomography (PET), and computed tomography (CT) have been the
three major imaging modalities utilised for decades goni2022brain . For
vascular imaging, magnetic resonance angiography (MRA) is one of the most
common choices due to its noninvasive nature, absence of ionising radiation
exposure, capability for non-contrast examination, and capacity to provide
volumetric representations that highlight vascular disease kuo2019vascular .

However, the complexity of vascular imaging data and the anatomical
variability among subjects make vessel segmentation a challenging task.
Despite the clinical need, a fully automated segmentation method for 3D
vascular segmentation and subsequent feature extraction has not yet been
developed due a number of specific challenges deshpande2021automatic . The
main hurdles are the variations across scales, differences in individual
anatomies, complexities of slender structures, a limited volume ratio (i.e.
vessel/background), and a scarcity of publicly available labeled data. In
terms of individual anatomical variations, there are significant disparities
in vessel length, diameter, and tortuosity, complicating the tasks of vascular
tracking and segmentation deshpande2021automatic ; luo2006extraction ;
lesage2009review ; zhao2017automatic . Additionally, narrower vessels with
diameters at the border of the imaging resolution are particularly difficult
to capture deshpande2021automatic ; ajam2017review .

Hierarchical Phase-Contrast Tomography (HiP-CT) walsh2021imaging  is a nascent
imaging technology developed in 2020 on the BM05 bealine at the ESRF. The
technique is a propagation-based phase-contrast tomography technique that
utilises the increased brilliance and resulting high spatial coherence of the
European Synchrotron Radiation Facility’s upgrade to a 4th Generation X-ray
source (The Extremely Brilliant Source (EBS). With HiP-CT, it is possible to
resolve very small refractive index difference (density difference) in soft
tissue structures, even in very large samples such as whole adult human
organs. The propagation through free space of the X-rays, after interaction
with the sample allow the refractive index shifts to be converted to intensity
variation through single-distance phase retrieval at reconstruction
paganin2002simultaneous . HiP-CT has been used to image whole human organs,
down to resolution of some single-cell types in regions of interest
walsh2021imaging .

Figure 1: Figure after walsh2021imaging , HiP-CT of brain (a), lung (b), heart
(c) kidney (d) and spleen (e); for each organ, a 3D rendering (i) of the whole
organ is shown using scans at 25$\mu$m per voxel. Subsequent 2D slices (ii–iv)
show positions of the higher-resolution VOI relative to the previous scan.
(v), Digital magnification of the highest-resolution image with annotations
depicting characteristic structural features in the brain (ml, molecular
layer; gl, granule cell layer; pc, Purkinje cell), in the lung (c, blood
capillary; ec/m, epithelial cell or macrophage), in the heart (mc, myocardium;
ca, coronary artery; ad, adipose tissue), in the kidney (e/a, efferent or
afferent arteriole; g, glomerulus) and in the spleen (rp, red pulp; wp, white
pulp; a, arteriole; ss, splenic sinus). All images are shown using 2× binning.

A specific novelty of HiP-CT imaging as it relates to the vasculature, lies in
its hierarchical nature. Whole human organs can be imaged with overview
resolution of $\sim 20\mu{m}/$voxel, then followed by high resolution (down to
$\sim 2\mu{m}/$voxel) regions of interest anywhere within the sample without
sectioning (Figure 1). This unique feature allows researchers to image the
hierarchical structure of the vascular tree from the largest vessels down to
near the capillary bed in intact human organs. This novel capability has
already allowed unique insights into vascular abnormalities in COVID-19 lung
lobes to be identified ackermann2020pulmonary ; ackermann2022fatal ;
mentzer2022endothelialitis , as well as vascular quantification across the
whole organ rahmani2023micro . Despite its advantages and suitability for
vessel imaging, HiP-CT also poses many challenges for CNN-based methods of
vascular segmentation:

  * •

large data volume (routinely $\geq$ 1Tb) require cutting-edge hardware

  * •

multi-scale vessels to segment

  * •

lack of available ground truth training data due to the novelty of the method

  * •

phase contrast generates fringes in intensity at vessel boundaries

  * •

large vessels can collapse

  * •

no targeted vascular staining to enhance contrast between vessel lumen and
surrounding tissue

  * •

continually developing technique with ongoing changes in image quality (e.g.
improvement to resolution, SNR and CNR) result in heterogeneous datasets

In light of the opportunity and challenges that HiP-CT poses for vascular
research, we perform a review of the literature on blood vessel segmentation
with a particular focus on identifying current models that are most suited to
the segmentation of blood vessels from HiP-CT imaging. We review segmentation
on an organ-by-organ basis as the vascular and parenchymal tissue structures
surrounding the vasculature differ vastly between different tissues. Our
ultimate aim is to identify and then develop a model and training strategy
which will provide a robust and generalisable method that can be easily
adapted to segment multi-scale vascular structures across all organs imaged by
HiP-CT.

This paper is arranged as follows: In Section 2, deep learning models used for
vessel segmentation, the datasets used in their training, and the evaluation
metrics in the literature are briefly explained. In Section 3, the most
prominent method in the literature is tested on HiP-CT kidney vascular data to
establish a baseline of performance and highlight specific challenges in the
adaptation of these models to HiP-CT data. In the final Section, the
discussion and conclusions are presented.

##  2 Vessel segmentation

###  2.1 Modalities for 3D vascular imaging

Whilst the ideal vessel segmentation algorithm or model is robust across a
range of imaging modalities, in reality, the imaging modality will impose
contrast and resolution limits that must be considered in segmentation
approaches as well as imaging-specific artefacts (e.g anisotropic voxels).
Here, we review some of the most common imaging modalites for vascular
networks and contextualise the development of HiP-CT within this field.

The most widely adopted procedure for visualising vessels in human organs in
vivo is angiography, i.e. where a contrast agent is injected into the vessels.
The major angiographic modalities utilised in clinical practice are computed
tomography angiography (CTA), magnetic resonance angiography (MRA), and
digital subtraction angiography (DSA) also known as a conventional angiogram.

MRA emerged as the predominant modality in vessel segmentation studies, as
evidenced in Tables 1, 2, 3, and 4. Among the 31 deep learning-based vessel
segmentation studies we reviewed across various organs, 9 employed MRA.
Notably, for brain vessel segmentation, 9 out of the 12 studies utilised this
modality. This is largely attributed to the fact that commonly available
datasets for brain vasculature studies, such as PEGASUS mutke2014clinical ,
MIDAS bullitt2005vessel , SCAPIS bergstrom2015swedish , and 1000PLUS
hotter2009prospective , utilise MRA.

MRA can be divided into three categories depending on the technique behind the
image acquisition / generation: TOF (time-of-flight), PC (phase contrast), and
CE (contrast-enhanced). Among those, TOF-MRA is the most frequently used non-
contrast bright-blood technique for imaging the human vascular system
forkert2009automatic ; bollmann2022imaging . As suggested by its name, TOF MRA
relies on a principle known as flow-related enhancement, which happens when
completely magnetised blood flows into a slab of magnetically saturated tissue
whose signal has been muted by repeated RF-pulses baghaie2018curvelet .
However, the long acquisition time and high operational costs make it
difficult to employ for cerebral artery visualisation klimont2020deep ;
katz1995circle . Furthermore, MRA has been shown to overestimate stenosis
compared to other modalities buerke2009dual .

CTA, on the other hand, is faster, takes a few minutes to complete, and is
more accurate than MRA. However, unlike MRA, all CTAs need the administration
of an IV contrast agent and involve radiation exposure, making it less safe.

DSA has been utilised as the gold standard for imaging vessels; nonetheless,
this invasive and labor-intensive method is rather costly and comes with
discomforts and possible dangerswillinsky2003neurologic ;
kaufmann2007complications . For the in vivo methods described above the
resolution of imaging is on the order of 0.5-2mm bollmann2022imaging ;
willinsky2003neurologic , with the state-of-the-art TOF techniques able to
capture at most (300$\mu{m}$ \- 500$\mu{m}$). Capturing the microvasculature
(1-100$\mu{m}$ ) is only possible in vivo at very shallow depths using e.g.
contrasted ultrasoundkierski2020perspectives , or must be performed ex vivo on
extracted tissue using optical or X-ray methods jafree2019spatiotemporal ;
reichardt20213d . Even within the ex vivo techniques, there are only two,
Light-sheet imaging and HiP-CT capable of imaging intact human organ vascular
networks that bridge the length scales from large arteries and veins down to
the capillary bed zhao2020cellular ; walsh2021imaging . Of the two techniques,
HiP-CT can perform multi-scale imaging far more quickly than light sheet
imaging as HiP-CT does not rely on staining of the vasculature shortening
sample preparation, rahmani2023micro , and imaging itself of a whole organ at
20$\mu{m}$ /voxel can be performed in as little as 1 hr rahmani2023micro .
Whilst the acquisition of these large vascular network datasets is becoming
increasingly fast, automated segmentation has also been a rapidly developing
area of research wagner20213d .

Table 1: Review of the papers applying deep learning for brain vessel
segmentation. CNN: Convolutional neural network; GAN: Generative adversarial
network; cGAN: Conditional GAN; DD-Net: Dense-dilated neural network; LSTM:
Long short-term memory; U-Net MSS: Multi-scale supervised U-Net; TOF MRA: Time
of flight magnetic resonance angiography; DSA: Digital subtraction
angiography; $\mu$CTA: Micro-computed tomography angiography; 3DRA: 3D
rotational angiographies; DSC: Dice similarity coefficient; HD: Hausdorff
distance; AVD: Average distance; Prec: Precision; MHD: Mahalanobis distance;
IOU: Intersection over union; PPV: Positive predictive value; CAL:
Connectivity-area-length.

References | Modality | Data source | No. of subj. | ML model | Input | DSC | Add’l perf. metrics
---|---|---|---|---|---|---|---
Livne et al. livne2019u  | TOF MRA |  | Private
---
(PEGASUS)
66 | U-Net | 2D patches | 0.88 |  | 95HD = $\sim$47 voxels
---
and an
AVD = $\sim$0.4 voxels
Phellan et al. phellan2017vascular  | TOF MRA | Private | 5 | 2D CNN | 2D patches |  | between 0.764
---
and 0.786
-
Hilbert et al. hilbert2020brave  | TOF MRA |  | Private
---
(PEGASUS+
7UP+
1000Plus)
264 |  | 3D CNN
---
(BRAVE-NET)
3D patches | 0.931 |  | 95HD = 29.153,
---
and AVD = 0.165
Patel et al. patel2020multi  | DSA | Private | 100 |  | 3D CNN (DeepMedic)
---
and 3D U-Net
3D patches |  | 0.94±0.02
---
and 0.92±0.02
respectively
| CAL= 0.84±0.07
---
and 0.79±0.06
respectively
Tetteh et al. tetteh2020deepvesselnet  |  | TOF MRA
---
and $\mu$CTA
| Publicly
---
available
Synthetic data |  | NN with 2D
---
orthogonal
cross-hair filters
(DeepVesselNet)
3D volumes | 0.79 |  | Prec=0.77
---
and Recall=0.82
Garcia et al. garcia2022deep  | 3DRA | Private | 5 |  | 3DUNet-based
---
architectures
3D patches | 0.80 ± 0.06 |  | Prec=0.75 ± 0.10
---
and Recall=0.90 ± 0.07
Vos et al. de2021automatic  | TOF-MRA | Private | 69 |  | 2D and
---
3D U-Net
| 2D and
---
3D patches
| 0.74 ± 0.17
---
and 0.72 ± 0.15
respectively
| MHD=47.6 ± 40.4
---
and 5 81.3 ± 57.0
respectively
Chatterjee et al. chatterjee2020ds6  | TOF MRI |  | Not explicity
---
stated
11 | U-Net MSS | 3D patches | 0.79 ± 0.091 | IOU=65.89±1.25
Zhang and Chen zhang2019ddnet  | TOF-MRA |  | Not explicity
---
stated
42 | DD-Net | 3D patches | 0.67 |  | Sensitivity=67.86
---
and IOU=33.66
B.Zhang et al. zhang2020cerebrovascular  | TOF-MRA |  | Publicly available
---
(MIDAS)
109 | DD-CNN | 3D patches | 0.93 |  | PPV=96.4730,
---
Sensitivity=90.1443,
and Acc=99.9463
Lee et al. lee2021spider  | MRA |  | Private
---
(SNUBH)
26 |  | 2D U-Net with LSTM
---
(Spider U-Net)
‘2D images | 0.793 | IOU=74.3
Quintana et al. quintana2022dual  | TOF-MRA |  | Private
---
(UNAM)
4 | Dual U-Net-Based cGAN | 2D images | 0.872 | Prec=0.895

Table 2: Review of the papers applying deep learning for kidney vessel
segmentation. CNN: Convolutional neural network; GAN: Generative adversarial
network; WSI: Whole slide imaging; DPA: Deep priori anatomy; MCD: Mean
centerline distance; EnMcGAN: Ensemble multi-condition GAN; DUP-Net: Double
UPoolFormer networks; DSC: Dice similarity coefficient.

References | Modality | Data source | No. of subj. | ML model | Input | DSC | Add’l perf. metrics
---|---|---|---|---|---|---|---
Karpinski et al. salvi2020karpinski  | WSI | Publicly available | 35 |  | 2D UNet with
---
Resnet34 backbone
2D images | - | Acc: 0.893
He et al. he2019dpa  | CT | Private | 170 | DPA-DenseBiasNet | 3D volumes | 0.861 | MCD:1.976
Taha et al. taha2018kid  | CT | Private | 99 | Kid-Net (a 3D CNN) | 3D patches | - |  | F1 score: 0.72 (artery);
---
0.67 (vein)
He at al. he2021enmcgan  | CTA | Private | 122 | EnMcGAN | 3D patches |  | 0.89±0.6 (artery);
---
0.77±0.12 (vein)
-
Zhang et al. zhang2022novel  | CT | Publicly available | 392 | DPA-DenseBiasNet | 2D images | 0.884 | -
Xu et al. xu2023extremely  | micro-CT | Private | 8 | CycleGAN | 3D patches | 0.768 ± 0.3 | Acc: 0.992
Li et al. li2023dup  | CT | Publicly available | 35 | DUP-Net | 3D patches | 0.883 |  | Precision:0.911;
---
Recall:0.858

Table 3: Review of the papers applying deep learning for coronary vessel
segmentation. DSC: Dice similarity coefficient; Prec: Precision; Sen:
Sensitivity; Spec: Specificity; GBDT: Gradient boosting decision tree;
CCTA:Coronary computed tomographic angiography; XCA: X-ray coronary
angiography; GCN: Graph convolutional networks; MSD: Mean surface distance;
AUC: Area under the receiver operating characteristic curve; ROI: Region of
interest; 3D FFR U-Net: 3D feature fusion and rectification U-Net

References | Modality | Data source | No. of subj. | ML model | Input | DSC | Add’l perf. metrics
---|---|---|---|---|---|---|---
Dong et al. dong2022novel  | CCTA | Private | 338 | Di-Vnet | 3D patches | 0.902 |  | Prec=0.921,
---
Recall=0.97
Gao et al. gao2022vessel  | XCA | Private | 130 | GBDT | 2D images | - |  | F1=0.874,
---
Sen= 0.902,
Spec=0.992
Wolterink et al .wolterink2019graph  | CCTA |  | Publicly
---
available
18 | GCN | 2D images | 0.74 | MSD=0.25mm
Li et al. li2022automatic  | CCTA | Private | 243 | 2D U-Net with 3DNet | 2D images | 0.771 ± 0.021 | AUC=0.737
Song et al. song2022automatic  | CCTA | Private | 68 | 3D FFR U-Net | 3D patches | 0.816 |  | Prec=0.77,
---
Recall=0.87
Zeng et al. zeng2023imagecas  | CCTA |  | Publicly
---
available
1000 | 3D-UNet | 3D volumes | 0.82 | -

Table 4: Review of the papers applying deep learning for pulmonary vessel
segmentation. CNN: Convolutional neural network; CT: computed tomography; CTA:
computed tomography angiography; MSI-U-Net: Multi-scale interactive U-Net; OR:
Over segmentation rate; AUROC: Area under the receiver operating
characteristic curve; Sen: Sensitivity; Spec: Specificity; Prec: Precision.

References | Modality | Data source | No. of subj. | ML model | Input | DSC | Add’l perf. metrics
---|---|---|---|---|---|---|---
Tan et al. tan2021automated  | CT and CTA |  | Publicly
---
available
16 |  | 2D-3D U-Net
---
nnU-net
| 2D images
---
3D volume
| 0.786,
---
0.797,
0.77
(on CT)
| OR=0.281,
---
0.285,
0.304
Nam et al. nam2021automatic  | CTA | Private | 104 | 3D U-Net | 3D patches | 0.915±0.31 | AUROC=0.995
Guo et al. guo2021comparison  | CT | Private | 50 | 3D CNN | 3D patches | 0.943 | -
Xu et al. xu2018pulmonary  | CT | Private |  | 2D CNN |  | - | -
Nardelli et al. nardelli2018pulmonary  | CT |  | Publicly
---
available
55 | 3D CNN |  | 2D and
---
3D patches
- |  | Sen=0.93
---
Prec=0.83
Wu et al. wu2023multi  | CT | Private | 143 | MSI-U-Net | 3D volume | 0.7168 |  | Sen=0.7234,
---
Prec=0.7893

Before the arrival of complex machine learning methods, vessel segmentation
relied heavily on traditional techniques such as kernel-based methods,
tracking methods, mathematical morphology-based methods, and model-based
methods taha2015metrics . In kernel-based methods, edge detection techniques,
like the Canny or Sobel operators, identify vessel boundaries using gradient
information chatterjee2021retinal . More sophisticated approaches, such as
Hessian-based methods can be categorised under model-based methods
foruzan2012hessian . These methods utilise second-order intensity derivatives
to capture vessel-like structures, and level-set methods evolve contours
within the image domain to detect vessels. Active contours, or snakes, are
tracking methods dynamically adjusted to capture vessel boundaries, and
mathematical tools like B-splines offer precise modeling of vessel structures
zhao2015automated ; shang2010vascular .

These traditional methods provided a foundation that paved the way for machine
learning in the realm of medical image analysis.

With the rapid advancement of computational capabilities, a paradigm shift has
taken place in the realm of image processing and machine learning: the rise of
deep neural networks (NNs). These NNs, employing multi-layered architectures,
extracted patterns within data through multiple levels of abstraction, and
transforming the way images are understood and processed.

Among various NN architectures, CNNs have stood out as highly effective for
image analysis tasks. They possess the capability to learn image features in a
hierarchical manner, progressing from simple to complex, thereby eliminating
the need for manual feature extraction. This inherent feature has made CNNs
especially popular in the field of medical image analysis.

Recently, the landscape of computer vision has seen another transformative
shift with the emergence of Transformers vaswani2017attention . Initially
successful in natural language tasks, Transformers have begun to find
application in various computer vision challenges, prompting researchers to
reevaluate the dominant role of CNNs. These advancements in computer vision
have also generated significant interest in the medical imaging domain
shamshad2023transformers . Transformers, with their ability to capture a more
extensive global context compared to CNNs have now become a subject of
considerable attention in the medical imaging community al2023vision ;
he2023transformers .

###  2.2 Evaluation metrics

In order to evaluate any automated vessel segmentation model or algorithm, a
quantitative assessment of a segmentation task can be done through various
performance metrics. Depending on the theory behind them, these metrics can be
divided into overlap-based, volume-based, pair-counting-based, information-
theoretic-based, probabilistic-based, and spatial distance-based measures
taha2015metrics . These are summarised in Figure 2

When assessing the performance of segmentation algorithms in comparison to
ground truth, a contingency table is often employed, featuring True Positive
(TP), True Negative (TN), False Negative (FN), and False Positive (FP) values.
In this context, positive and negative denote pixels that, according to the
ground truth segmentation, are attributed to vessels and background
respectively.

A review of the literature reveals varied metric reporting across studies (as
evidenced in Tables 1, 2, 3, and 4). This section delves into the most
frequently used performance metrics, elucidating their underlying theory,
strengths, and weaknesses.

Figure 2: Illustration for calculation of volumetric Dice score coefficient
(DSC), Hausdorff distance, and surface DSC. The solid line represents the
ground truth contour, whereas the dashed line is the prediction. i. A
Volumetric DSC, defined as the union of two volumes (green volume region)
normalised by the mean of the two volumes. ii. Hausdorff distance, defined as
the maximum nearest neighbor Euclidean distance (green arrow). iii. Surface
DSC, is defined as the union of two contours (yellow contour region)
normalised by the mean surface of the two contours. - after
vaassen2020evaluation

Overlap-Based Measures Overlap-based measures are used to evaluate the
similarity between the segmented result and the ground truth based on their
overlapping regions. Such measures are the most widely utilised metrics with
all studies we reviewed reporting at least one overlap based metric. However
they have some limitations when applied to vascular segmentation which will be
discussed further. The Dice Similarity Coefficient (DSC) is one of the most
common overlap-based metrics that calculates the spatial overlap of two
segmentations 26 of the 31 studies reviewed here utilise DSC. Given its
properties, DSC is particularly used in vascular segmentation where the
vasculature accounts for a small fraction (e.g. around 3% in the brain) of the
organ volume, leading to unbalanced data challenges nicholson2001diffusion .

$Dice=\frac{2\cdot TP}{FP+FN+(2\cdot TP)}\mbox{.}$ (1)

Sensitivity, also known as TP Rate or Recall, is another overlap-based metric.
It measures how well a model identifies TPs (e.g., correctly segmented
vessels). It provides insight into how much of the ground truth overlaps with
the predicted positive segment. Specificity, on the other hand, measures the
proportion of TNs (e.g., correctly identified non-vessel areas) that are
correctly detected. It reflects on how much of the ground truth’s negative
area overlaps with the predicted negative segment. These two measures are
defined as follows:

$Sensitivity=\frac{TP}{TP+FN}\mbox{,}$ (2)

$Specificity=\frac{TN}{FP+TN}\mbox{.}$ (3)

Another related measure is precision, often referred to as the positive
predictive value (PPV). While not frequently utilised in medical image
validation, it plays a role in determining the F-Measure. This is defined as

$Precision=\frac{TP}{TP+FP}\mbox{,}$ (4)

whereas F-Measure, also known as F1 score is the harmonic mean of precision
and recall.

In 2020, Shit et al. developed centerline Dice (clDice), a unique topology-
preserving loss function for tubular structure segmentation, as another DSC
modification shit2021cldice . Their work highlighted that DSC metric does not
necessarily assess the connectedness of a segmentation because Dice does not
equally weight tubular structures with large, medium, and small radii. As
connectivity is a crucial biological feature of vascular networks, bespoke
metrics should be used to evaluate vascular segmentations. The authors
demonstrated that training on a globally averaged loss causes a considerable
bias towards the volumetric segmentation of big arteries in real vascular
datasets compared to arterioles and capillaries (radius ranges e.g., 30 $\mu$m
for arterioles and 5 $\mu$m for capillaries). To enable topology preservation,
centerline Dice is formulated as follows:

$T_{\text{prec}}=\frac{\mid S_{P}\cap V_{L}\mid}{\mid S_{P}\mid}\mbox{,}$ (5)

$T_{\text{sens}}=\frac{\mid S_{L}\cap V_{P}\mid}{\mid S_{L}\mid}\mbox{,}$ (6)

$clDice=\frac{2\times T_{\text{prec}}\times
T_{\text{sens}}}{T_{\text{prec}}+T_{\text{sens}}}\mbox{,}$ (7)

where the ground truth mask is $V_{L}$, the predicted segmentation mask is
$V_{P}$ and skeletons are $S_{P}$ and $S_{L}$.

Volume-Based Measures These metrics focus on the volume or size of the
segmented structures. As the name implies, Volume Difference (VD) is a typical
volume-based metric that is based on the absolute difference in volumes of the
segmented structure and the ground truth, usually normalised by the ground
truth volume. Contrary to a simple volumetric subtraction, VD accounts for the
volumetric overlap by considering voxels exclusive to each mask as well as
those shared. This ensures that VD accurately reflects both the volume and the
spatial agreement between the two segmentations. VD is formulated as follows:

$VD=\frac{\lvert V_{p}-V_{g}\rvert}{V_{g}}\mbox{,}$ (8)

where $V_{p}$ is the volume of the predicted segmentation and $V_{g}$ is the
volume of the ground truth.

Pair-Counting-Based Measures The pair counting based measures are calculated
based on the correspondence between object pairs in the segmentation and the
ground truth. One such metric is Adjusted Rand Index (ARI) and it is
calculated as follows:

$\text{ARI}=\frac{\text{RI}-\text{Expected\\_RI}}{\text{Max\\_RI}-\text{Expected\\_RI}}\mbox{,}$
(9)

where RI is the Rand Index and is calculated as:

$\text{RI}=\frac{\text{a}+\text{d}}{\text{a}+\text{b}+\text{c}+\text{d}}\mbox{,}$
(10)

where a is the number of pairs of objects that are in the same group in both
the predicted and the ground truth and corresponds to the TPs; d is the number
of pairs of objects that are in different groups in both and corresponds to
the TNs; and b and c correspond to FPs and FNs, respectively.

Information-Theoretic-Based Measures As the name implies, information-
theoretic-based measures use information theory concepts to estimate the
quality and performance of the segmentation. For instance, one such measure
called Mutual Information (MI) is calculated based on the shared information
between the segmented result and the ground truth.

For two discrete random variables X (segmentation result) and Y (ground
truth), the MI is defined as:

$MI(X,Y)=\sum_{x\in X}\sum_{y\in
Y}p(x,y)\log\left(\frac{p(x,y)}{p(x)p(y)}\right)\mbox{,}$ (11)

where

  * •

    * $\ignorespaces p(x,y)$

is the joint probability distribution function of  $\ignorespaces X$ and
$\ignorespaces Y$ .

  * •

    * $\ignorespaces p(x)$

is the marginal probability distribution function of  $\ignorespaces X$ .

  * •

    * $\ignorespaces p(y)$

is the marginal probability distribution function of  $\ignorespaces Y$ .

  * $\ignorespaces X$

might represent the predicted segmentation where
a specific value  $\ignorespaces x$ taken by  $\ignorespaces X$ could be
either “object” (e.g., a vessel) or “background.” and  $\ignorespaces Y$
represents the ground truth (actual segmentation) where
a specific value  $\ignorespaces y$ taken by  $\ignorespaces Y$ could
similarly be either “object” or “background.”

In that context:

  * •

    * $\ignorespaces p(x=\text{object},y=\text{object})$

corresponds to the probability of a TP.

  * •

    * $\ignorespaces p(x=\text{background},y=\text{background})$

corresponds to the probability of a TN.

  * •

    * $\ignorespaces p(x=\text{object},y=\text{background})$

corresponds to the probability of a FP.

  * •

    * $\ignorespaces p(x=\text{background},y=\text{object})$

corresponds to the probability of a FN.

Probabilistic-Based Measures

Probabilistic-based measures evaluate the performance of segmentation and
classification algorithms based on predicted probabilities rather than strict
binary or discrete decisions. In binary classification, for example, a
probabilistic model may assign a probability indicating the likelihood that an
item belongs to the positive class rather than simply categorizing it as
positive or negative.

The ROC curve (Receiver Operating Characteristic) stands as a prime example of
a probabilistic-based measure. It graphs the TP rate (sensitivity) against the
TN rate (specificity) over a spectrum of decision thresholds. This curve
offers a holistic perspective on a model’s capability to distinguish between
classes.

The Area Under the Curve (AUC) encapsulates the model’s overall discriminative
prowess between positive and negative classes. From a mathematical standpoint,
the AUC represents the integral of the ROC curve:

$\text{AUC}=\int_{0}^{1}\text{TPR}(t)dt\mbox{,}$ (12)

where  $\ignorespaces\text{TPR}(t)$ is the TP rate at a given threshold
$\ignorespaces t$ . This metric or variations appear in 3 of the 31 models
reviewed in Tables 1, 2, 3, and 4).

Spatial Distance-Based Measures

Spatial distance-based measures focus on the spatial discrepancies between the
segmented structures and the ground truth. For instance, the 95th Percentile
Hausdorff Distance (95HD) assesses the greatest distance between a point in
the true segmentation and the nearest point in the segmented result. More
formally, if we define  $\ignorespaces h(A,B)$ as the set of all distances
from points in set  $\ignorespaces A$ to their nearest points in set
$\ignorespaces B$ , then:

$\text{95HD}(A,B)=\text{percentile}(h(A,B),95)\mbox{.}$ (13)

The Average Volumetric Distance (AVD), on the other hand, is typically
computed as the absolute difference in volumes between the predicted
segmentation  $\ignorespaces V_{p}$ and the ground truth  $\ignorespaces
V_{gt}$ , normalised by the volume of the ground truth. Mathematically, it can
be expressed as:

$\text{AVD}=\frac{\mid V_{p}-V_{gt}\mid}{V_{gt}}\times 100\mbox{.}$ (14)

Boundary-based techniques are another important subcategory of distance-based
measures maier2022metrics . Normalised surface Dice, mean average surface
distance, and average symmetric surface distance are three key metrics,
especially in the field of vessel segmentation. Surface dice is a measurement
to evaluate the similarity between the segmented surface and a ground truth
surface. Normalised surface Dice takes this a step further by normalising the
measurement and making it less sensitive to size differences between the
predicted segmentation and the ground truth. This is particularly useful for
segmenting smaller structures, or size variability between subjects. Surface
distance is another measurement that indicates the shortest distance of each
point on the surface of the segmented structure to the surface of the ground
truth and is calculated as follows:

$\text{ASSD}(A,B)=0.5\left(\frac{1}{\mid A\mid}\sum_{a\in A}\min_{b\in
B}\text{dist}(a,b)+\frac{1}{\mid B\mid}\sum_{b\in B}\min_{a\in
A}\text{dist}(b,a)\right)\mbox{,}$ (15)

where dist(a, b) is the Euclidean distance between points $a$ and $b$, and
$\mid A\mid$ and $\mid B\mid$ are the numbers of surface points in A and B
respectively.

Of the 31 paper reviewed 5 use a spatial distance metric.

The use of the appropriate assessment metric is crucial, as each measurement
has specific biases dependent on the characteristics of the segmented
structures. Whilst our review of literature shows that DCS or other
generalised overlap measures are still the most frequently used metrics, more
specialised vascular specific metrics are available and are becoming more
widely implemented. The evaluation metric can strongly influence the choice of
the final optimal model and hence should be chosen in accordance with the
segmentation task’s nature and taking into consideration what downstream
analyses are to be conducted on the segmentations, or the biological
implications of a particular metric. For example for vascular structures,
quantitative analyses of network topology for flow simulations are common end
goals for segmentation rahmani2023micro ; sweeney2018role . In such cases
meshing or skeletonising are common steps after segmentation. Meshing or
skeletonisation algorithms are often highly sensitive to holes or cavities in
the segmentations, or to breaks in connectivity in vascular
componentszhang2022techniques . Thus, evaluation methods highly sensitive for
connectivity breaks (e.g. clDice) should be implemented in these cases.

###  2.3 Deep learning models for lung, kidney, cardiovascular, and cerebral
vessel segmentation

In this section, we delve into the latest deep-learning (DL) strategies used
for blood vessel segmentation. We’ve grouped the DL-based techniques for
segmenting vessels under three primary network structures: Convolutional
Neural Networks, Generative Adversarial Networks, and Vision Transformers.

####  2.3.1 Convolutional neural network based models (CNNs)

Traditional CNNs have been foundational in vessel segmentation before the
widespread adoption of architectures like U-Net. These neural networks, based
on fundamental concepts of learning features and building hierarchical
representations, leverage the presence of spatial structures and patterns
inherent in image data. Unlike the Fully Convolutional Networks (FCN) that
perform dense predictions, traditional CNNs often operate on patches or
regions, aiming at classifying central pixels or aggregates.

Yao et al. used a 2D CNN architecture to extract blood vessels from fundus
images yao2016convolutional . The output of their CNN architecture is the
confidence level of each pixel being blood vessels. In 2018, Tetteh et al.
developed an architecture called DeepVesselNet. Their design leverages 2-D
orthogonal cross-hair filters using 3-D context information while minimizing
computational demands. Addinionally, they introduce a class balancing cross-
entropy loss function with false positive rate correction specifically
tailored to address the prevalent issues of significant class imbalances and
high false positive rates observed with traditional loss functions
tetteh2020deepvesselnet .

Cervantes-Sanchez et al. trained a multilayer perceptron with X-ray Coronary
Angiography (XCA) images enhanced by using Gaussian filters in the spatial
domain and Gabor filters in the frequency domain for segmentation of coronary
arteries in X-ray angiograms cervantes2019automatic . Nasr-Esfahani et al.
presented a multi-stage model where a patch around each pixel is fed into a
trained CNN to determine whether the pixel is of vessel or background
regions.nasr2018segmentation . Samuel and Veeramalai proposed a two-stage
vessel extraction framework to learn well-defined vessel features from global
features (learned by the pre-trained VGG-16 base network) using the Vessel
Specific Convolutional (VSC) blocks, Skip chain Convolutional (SC) layers, and
feature map summations samuel2021vssc . In 2021, Iyer et al. developed a new
CNN for angiographic segmentation: AngioNet, which combines an Angiographic
Processing Network (APN) with Deeplabv3+ because of its ability to approximate
more complex functions. The APN was tailored to tackle several challenges
specific to angiographic segmentation, including low-contrast images and
overlapping bony structuresiyer2021angionet .

Figure 3: A sample U-Net architecture used in medical image segmentation tasks
- after ronneberger2015u

##### Fully convolutional neural network (FCN) based models: U-Net and its
variants

Fully Convolutional Networks (FCNs) are a subtype of CNNs specialised in
semantic segmentation tasks. Deep learning techniques have revolutionised
medical image segmentation, achieving precise pixel-level classification with
the help of FCN long2015fully . FCNs are designed to produce an output of the
same size as the input (i.e., a pixel-wise map). They replace the dense layers
with convolutional layers. Expanding upon FCN’s foundation, many researchers
have pioneered advanced 2D fully convolutional neural networks, including
U-Net, SegNet, DeepLab, and PSPNet ronneberger2015u ; badrinarayanan2017segnet
; chen2017deeplab ; zhao2017pyramid . Khan et al., presented a fully
convolutional network called RC-Net for the purpose of retinal image
segmentation. The network itself is relatively small and the number of filters
per layer is optimised to reduce feature overlapping and complexity as
compared to alternatives elsewhere in the literature. In the model, they kept
pooling operations to a minimum and integrated skip connections into the
network to preserve spatial information khan2021rc .

U-Net, distinguished by its unique U-shaped architecture, has become
particularly popular for medical image segmentation tasks, especially when the
dataset is limited ronneberger2015u . It leverages skip-layer connections
combined with encoder-decoder multi-scale features, enhancing segmentation
precision. An example of a Unet architecture is illustrated in Figure 3.

Given U-Net’s success in medical image segmentation, Livne et al. streamlined
it by halving the channels in each layer, coining it “half U-Net” for brain
vessel segmentation livne2019u . Even though there has been extensive
exploration of 2D methodologies in the literature (see Tables 1 especially in
coronary vessel segmentation, 2, 3, and 4), it is observed that relying solely
on 2D segmentation networks may overlook inter-slice relationships, leading to
subpar segmentation wu2023multi . Additionally, given the nonplanar nature of
blood vessels, attempting to segment them without adequate volumetric
information, such as on a single slice or in 2.5D scenarios, poses challenges.
Although direct comparison of different studies is hampered due to the use of
different datasets, the results from the studies detailed in Tables 1, 2, 3,
and 4 suggest that 3D models outperform 2D models. Specifically, 3D models
achieve an average Dice score of 0.83, compared to the 0.79 average for 2D
models, underscoring the significance of interslice relationships.

Consequentially, researchers have proposed 3D segmentation networks like 3D
U-Net, V-Net, and VoxResNet cciccek20163d ; milletari2016v ; chen2018voxresnet
. Huang et al. utilised 3D U-Net with data augmentation for liver vessel
segmentation and introduced a weighted Dice loss function to address the voxel
imbalance between vessels and other tissues huang2018robust .

To enhance the segmentation abilities of the U-Net network, several
modifications have been proposed. One notable advancement is the attention
U-Net, introduced by Oktay et al., which is designed to suppress irrelevant
regions in an input image while highlighting salient features useful for a
specific task.oktay2018attention . Bahdanau et al. originally conceived the
attention mechanism to tackle the challenges arising from using a fixed-length
encoding vector, which restricted the decoder’s access to input information
bahdanau2014neural . This mechanism mimics human attention, enabling the model
to concentrate on specific input areas while generating an output. The core
idea behind this mechanism is to assign different weights to different parts
of the input data, indicating how much “attention” each part should receive
relative to others when producing a specific output.

Building upon the U-Net framework, Zhou and colleagues implemented U-Net ++
with the premise that the network would face a more straightforward learning
challenge when the feature maps from both the decoder and encoder networks
have semantic similarities zhou2018unet++ . With this goal in mind, they
reconfigured the skip pathways to diminish the semantic disparity between the
encoder’s and decoder’s feature maps.

Sanches et al. melded 3D U-Net and Inception, dubbing it “Uception” for brain
vessel segmentationsanchesa2019cerebrovascular . Dong and colleagues
introduced a cascaded residual attention U-Net, termed CRAUNet, for a layered
analysis of retinal vessel segmentation. The architecture capitalizes on the
advantages of U-Net, coupled with cascaded atrous convolutions and residual
blocks that are further enhanced by squeeze-and-excitation features
dong2022craunet . Drawing inspiration from U-Net and DropBlock, Guo et al.
introduced the Structured Dropout U-net (SD-Unet) for coronary vessel
segmentation. This design amalgamates the U-Net and DropBlock frameworks to
omit specific semantic details, thereby preventing the network from
overfitting guo2019sd .In 2021, Pan et al. adopted a 3D Dense-U-Net model and
replaced the standard DSC loss function with the focal loss function to tackle
the issue of class imbalance in order to achieve fully automated segmentation
of the coronary artery pan2021coronary . In 2022, Wu and colleagues introduced
the Multi-Scale Interactive U-Net (MSI-U-Net), an enhancement of the 3D U-Net
that enhances the precision of segmenting smaller vessels wu2023multi . They
offered a strategy for interacting with information at multiple scales, where
features are transferred among vessels of varying sizes through shared
convolution kernel parameters, strengthening the relationship between small,
medium, and large vessels in lung CT scans.

nnU-Net isensee2021nnu  is one of the most widely utilised framework for
vessel segmentation tasks, consistently delivering impressive results. It is
considered the state-of-the-art for a wide variety of medical image
segmentation tasks, with the original or variant of nnU-Net winning
international biomedical challenges across 11 tasksgonzalez2023lifelong ;
isensee2021nnu . It has also recently proposed as the network for the
standardised evaluation of continual segmentation gonzalez2023lifelong .
Rather than introducing a novel model architecture, nnU-Net is designed to
autonomously adapt and configure the entire segmentation framework,
encompassing preprocessing, network architecture, training, and post-
processing, to suit any new task. This deep learning framework is known for
its flexibility, scalability, and ease of use in tackling various medical
imaging segmentation problems. To this end, it is frequently used to benchmark
new model approaches, or combined with other models as in ensembles to improve
performance.

It should be noted that as Moccia et al. moccia2018blood  and Garcia et al.
garcia2022deep  recently mentioned, the success of the segmentation approaches
is highly influenced not only by the algorithm but also by factors such as
imaging modalities the presence/absence of noise or artifacts, and the
anatomical region of interest. This makes direct comparisons among the studies
in the literature challenging. On top of using different modalities from CTA
to MRA only 29% of the herein reviewed papers used publicly available data,
further complicating efforts to replicate or compare their findings.

The data presented in Tables 1, 2, 3, and 4 reveals a predominant use of U-Net
or variants of CNNs in the majority of studies, accounting for 80% of the
cases.

####  2.3.2 Generative adversarial networks (GANs)

Goodfellow et al. introduced the Generative Adversarial Network (GAN) for
synthesizing images from random noise goodfellow2014generative . GANs
represent generative models designed to approximate real data distributions,
enabling them to generate novel image samples. GAN models are commonly
employed for tasks such as image-to-image translation (cross-modality
synthesis), image synthesis, and data augmentation. Comprising two distinct
networks, a generator and a discriminator, GANs function by pitting these
networks against each other during training. A sample GAN architecture can be
seen from Figure 4. The generator crafts artificial images to deceive the
discriminator, while the discriminator endeavours to distinguish genuine
images from fabricated ones, a process termed “adversarial training”. This
methodology can be adapted to train segmentation networks, where a generator
is tasked with generating segmented images, and the discriminator
differentiates between the predicted segmentation maps and the authentic ones.
This modification prompts the segmentation network to yield more anatomically
accurate segmentation maps luc2016semantic ; chen2020deep . Conditional GANs
(cGANs), on the other hand, are a modified version where the generator creates
images depending on specific conditions or inputs, which can be useful in
vessel segmentation.

Son et al. introduced a technique employing generative adversarial training to
create retinal vessel maps in the context of vascular segmentation. Their
method enhanced the segmentation efficacy by employing binary cross-entropy
loss during the training of the generator son2019towards . In 2020, K.B. Park
et al. introduced a novel architecture called M-GAN, which aimed to enhance
the accuracy and precision of retinal blood vessel segmentation by combining
the conditional GAN with deep residual blocks park2020m . The M-generator
incorporated two deep FCNs interconnected through short-term skip and longterm
residual connections, supplemented by a multi-kernel pooling block. This setup
ensured scale-invariance of vessel features across the dual-stacked FCNs. They
integrated a set of redesigned loss functions to optimise performance,
encompassing BCE, LS, and FN losses. Recently, Amran et al. introduced an
adversarial DL-based model for automatic cerebrovascular vessel segmentation
amran2022bv . Their BV-GAN model utilised attention techniques, allowing the
generator to focus on voxels more likely to contain vessels. This is achieved
by leveraging latent space features derived from a prior vessel segmentation
map, effectively addressing the issue of imbalance settings. In related
research, Subramaniam et al. introduced a 3D GAN-based approach for
cerebrovascular segmentation subramaniam2022generating . While their method
used GANs for dataset augmentation (generating an extensive set of examples
with self-supervision), Amran et al. aimed to enhance the U-NET-based
segmentation directly through the GAN technique. Finally, earlier in 2023, Xie
et al. introduced the MLP-GAN for brain vessel segmentation xie2023mlp . This
method divided a 3D brain vessel image into three separate 2D views (sagittal,
coronal, and axial) and processed each through distinct 2D conditional GANs.
Every 2D generator incorporated a modified skip connection pattern integrated
with the MLP-Mixer block. This design enhances the ability to grasp global
details.

Figure 4: A sample generative adversarial network architecture - after
kate2022fingan

####  2.3.3 Vision transformers

Transformers are primarily designed for sequence-to-sequence tasks but have
shown significant promise in various domains, including computer vision.
Vision Transformers (ViTs) are a type of neural network architecture that use
transformer mechanisms to process images. First presented by Google Research
in 2020, Vision Transformers have shown competitive results with traditional
CNNs on image classification tasks, even surpassing them in some benchmarks.
In Transformers, attention allows the model to consider other words in the
input sequence when encoding a particular word, leading to the capture of
long-range dependencies. Vision Transformers (ViTs) use the same principle on
image patches, allowing the model to consider distant parts of an image when
encoding a particular patch. Following their success in the realm of computer
vision, researchers began to explore the potential of vision transformers for
medical image segmentation tasks jain2023segmenting .

Instead of being processed by pixel values, images are segmented into
setsized, non-overlapping sections, such as 16x16 pixels for medical image
segmentation using ViTs. These sections are linearly transformed into singular
vectors, a procedure called tokenization. Subsequently, to preserve the
spatial context, positional embeddings are integrated with the tokenised
patches. These enhanced embeddings navigate through various layers of the
standard transformer encoder. To form a segmentation mask, a decoding method,
which might be an upsampling layer or an alternative transformer, is applied
to produce labels for each pixel in the image. A sample vision transformer
framework for vasculature segmentation is illustrated in Figure 5.

In 2021, Chen et al. came up with a model called TransU-Net, where a hybrid
CNN-Transformer architecture is created to leverage both detailed high-
resolution spatial information from CNN features and the global context
encoded by Transformers by treating the image features as sequences
chen2021transunet .

Pan et al. introduced the Cross Transformer Network (CTN), a new approach
designed to understand 3D vessel features while considering their overall
structure. CTN accomplishes this by combining the U-Net and transformer
modules, allowing the U-Net to be more globally aware and better handle issues
like disconnected or imprecise segmentation pan2022deep .

Yu et al. introduced two new deep learning modules, called CAViT (Channel
Attention Vision Transformer) and DAGC (Deep Adaptive Gamma Correction), to
solve the problem of retinal vessel segmentation. CAViT combines two
components: efficient channel attention (ECA) and the vision transformer
(ViT). The ECA module examines how different parts of the image relate to each
other, while the ViT identifies important edges and structures in the entire
image. On the other hand, the DAGC module figures out the best gamma
correction value for each input image. It does this by training a CNN model
together with the segmentation network, ensuring that all retinal images have
the same brightness and contrast settings yu2022vision .

Zhang and his team, on the other hand, introduced a model named TiM-Net for
efficient retinal vessel segmentation zhang2022tim . To capitalize on
multiscale data, TiM-Net takes in multiscale images after maximum pooling as
its inputs. Following this, they incorporated a dual-attention mechanism after
the encoder to minimize the effects of noisy features. Concurrently, they
utilised the MSA mechanism from the Transformer module for feature re-coding
to grasp the extensive relationships within the fundus images. Finally, a
weighted SideOut layer was created to complete the final segmentation.

The Swin Transformer is a modified version of the Vision Transformer (ViT),
designed to further adapt the transformer structure for image-related tasks
and boost its efficiency. “Swin” gets its name from “Shifted Window,”
reflecting a key feature of its design. In July 2023, Wu and his team
developed the Inductive BIased Multi-Head Attention Vessel Net (IBIMHAV-Net)
wu2023hepatic . The architecture is formed by extending the Swin Transformer
to 3D and merging it with a potent mix of convolution and self-attention
techniques. In their approach, they used voxel-based embedding instead of
patch-based, to pinpoint exact liver vessel voxels, while also utilizing
multi-scale convolution tools to capture detailed spatial information.

Figure 5: A sample vision transformer architecture for renal vasculature
segmentation

The analysis of Tables 1, 2, 3, and 4 indicates a less frequent application of
GANs and transformers (around 20%), suggesting that while U-Net and CNNs are
the preferred methods in the field, there is still room for exploration and
potential growth in the utilisation of GANs and transformers for vessel
segmentation.

##  3 nnU-Net for kidney vessel segmentation

From the above review of the literature, we concluded that segmentation of
blood vessels from HiP-CT data should be initially attempted using nnU-Net
isensee2021nnu . As nnU-Net is one of the most widely utilised frameworks for
vessel segmentation tasks and has consistently delivered impressive results,
we felt that this would provide the initial baseline against which any future
development of more HiP-CT-specific frameworks should be benchmarked.
Therefore, we prepared a training dataset from three different human kidneys,
imaged with HiP-CT and semi-manually segmented by expert annotators. The goal
was to assess how much training data is needed to provide adequate
segmentation for a single kidney but also to investigate if nnU-Net trained on
a subset of kidney data would generalise to a new kidney dataset given the
high inter-sample variability seen between human organs.

In this section, we will provide a description of the HiP-CT kidney datasets,
including its acquisition protocol and the segmentation process (subsection
3.1). Following that, we will present detailed information about the employed
nnU-Net framework configuration (subsection 3.2), and finally, we will present
the results for nnU-Net application to kidney vascular segmentation of HiP-CT
data subsection 3.3.

###  3.1 Hierarchical phase-contrast tomography (HiP-CT) kidney dataset

Table 5: The size of the kidney volumes from the dataset used in the
experiments together with their gender and resolution information

| Donor
---
identifier
| Experiment
---
name
| Dataset size
---
(x,y,z) (pixels)
Gender | Age |  | Scanning
---
voxel size
(um)
| Scan energy
---
(keV)
| Binned voxel size
---
(um)
LADAF-2021-17 | Kidney 1 | 1303,912,2279 | M | 63 | 25.0 | 81 | 50.0
S20-28 | Kidney 2 | 1041,1511,2217 | M | 84 | 25.0 | 88 | 50.0
LADAF-2020-27 | Kidney 3 | 1706x1510,500 | F | 94 | 25.08 | 93 | 50.16

Three human kidneys were used to create the training dataset - termed kidney
1, kidney 2 and kidney 3. Kidney 1 and kidney 3 , were collected from donors
who consented to body donation to the Laboratoire d’Anatomie des Alpes
Françaises before death. Kidney 2 was obtained after clinical autopsy at the
Hannover Institute of Pathology at Medizinische Hochschule, Hannover (Ethics
vote no. 9621 BO K 2021). The transport and imaging protocols were approved by
the Health Research Authority and Integrated Research Application System (HRA
and IRAS) (200429) and French Health Ministry. Post-mortem study was conducted
according to Quality Appraisal for Cadaveric Studies scale recommendations
wilke2015appraising . Sample preparation and scanning protocols are described
in the references brunet2023preparation ; walsh2021imaging ; rahmani2023micro
. Basic scan parameters and demographic information are provided in Table 5

The segmentation process of the three kidneys was carried out in Amira Version
2021.1. The reconstructed raw image data Figure6 B1 first underwent average
binning x2 Figure6 B2 from the acquired resolution ( ca. 25$\mu{m}$) to ca.
50$\mu{m}$, 3D median filtering was applied in Amira-Avizo v2021.1 (3
iterations 26 voxel neighbourhood) Figure6 B3 and filtering to visually
enhance vessels appearance performed using background detection correction
(Amira v2021.1; default parameter settings)Figure6B4. Segmentation was
performed in a semi-manual fashion using the Amira v2021.1 magic wand tool.
This is an interactive 3D region growing tool. Using this tool, annotators
select a seed voxel within a vessel in a slice (slices can be in any one of
three orthogonal directions); in addition, the annotator selects and refines a
combination of intensity threshold, contrast threshold and hard-drawn limits,
which are used to specify the stopping criteria of the 3D region growing. In
some cases, it is necessary to manually draw using a paintbrush tool in slice-
by-slice locations where vessels are infilled with blood or have largely
collapsed. To provide assurance for labelling quality, an expert annotation
validation process was implemented. Firstly, an independent, experienced
annotator meticulously conducted a 3D proofreading of the binary labels
filling in any missing vessel labels in the three orthogonal planes. After
this, five randomly selected 2D circular regions of the image are selected, a
third annotator marks all the labeled regions (one mark per region) (these
label regions are cross sections of the vessels). The third annotator then
counts the True Positive (correctly segmented vessels), False Negative (missed
vessels), (note false positives are rare and easily removed due to the
connected nature of the arterial tree). This provides a metric for what
proportion of vessels have been traced or missed, and by sampling random areas
in the dataset, directs the annotators to regions that may be poorly
segmented. This triple validation process was iterated to achieve final
segmentations which can be accessed via kaggle . It should be noted that with
the binning, median filter and manual approach, vessels with diameter as small
as 1-2 pixels could be segmented. Given the resolution of the images and their
location within the generation vascular tree, these vessels represent
interlobular arteries within the renal vascular tree and have been estimated
from higher resolution HiP-CT of the same kidney, to be two branching
generations from the kidney glomeruli (where the capillary bed is found). It
is also of note that these samples come from a diverse age grouping 63-94yr
old and have two male and one female represented. Finally it is also worthy of
note that in the case of kidney 2 (S20-28) the donor’s cause of death was
COVID-19 which can cause microthrombi in the renal vasculature and is seen as
hyperintensities particualrly around the edge of the kindey cortext, this can
be seen in Supplementary movies (1-3) which show slice by slice views of each
kidney.

Figure 6: Processing pipeline for HiP-CT imaging and segmentation of human
kidney vasculature. (A) Setup for imaging acquisition using HiP-CT at BM05; 1)
tomographic stage, 2) platform, 3) sample, 4) reference sample (B) Image
processing pipeline; 1) a 2D reconstructed image at 25 µm3 /voxel resolution;
2) binning the image by 2, 3) applying 3D median filter to increase signal-to-
noise ratio, 4) Image normalization using background detection correction, 5)
Segmentation and thresholding, 6) Labelling the four main arterial branches
(C) 3D rendering of the segmented vascular network of a human kidney. Each of
the main four branching of the renal artery entering the kidney are colour-
coded, Figure after rahmani2023micro

The number of slices and size of each slice segmentation were as followed (Z,
X, Y): kidney 1: (2279, 1303, 912); kidney 2: (2217, 1041, 1511); and kidney
3: (501, 1706, 1510). The network topologies automatically generated for 3D
full resolution configuration for experiment 1 were a patch size (Z, X, Y)of
[112,112,192], a batch size of 2 and the number of pool per axis (Z, X, Y)
were [4,4,5].

###  3.2 nnU-Net framework

Preprocessing. Image preprocessing is a necessary step in machine learning,
especially for image segmentation tasks. Notably, this step is essential in
deep learning since it prepares the input data for effective model training
and ensures that the model can learn meaningful patterns and relationships
from the data. Proper preprocessing can improve model performance,
generalisation, and robustness in handling real-world data. nnU-Net includes
some automated preprocessing steps as part of its data preparation pipeline.
These preprocessing steps include data augmentation (rotations, scaling,
gaussian noise, gaussian blur, brightness, contrast, simulation of low
resolution, gamma correction and mirroring), intensity normalization (global
dataset percentile clipping, z-score with global foreground mean, z-score with
per image mean), image and annotation resampling strategies (in-plane with
third-order spline, out-of-plane with the nearest neighbour, third-order
spline), and image target spacing (lowest resolution axis tenth percentile,
axes median, median spacing for each axis).

Architecture. It is important to mention that the nnU-Net framework does not
have a new deep learning architecture. However, it covers the U-Net family,
which has encoder-decoder architectures, including 2D U-Net ronneberger2015u ,
3D U-Net milletari2016v , and Cascaded 3D U-Net roth2018application . It also
provides an ensemble option, which explores 2D U-Net, 3D U-Net, or 3D cascade
results and chooses the best model (or combination of two) according to cross-
validation performance.

Postprocessing. nnU-Net framework also provides optional configuration for
postprocessing on the full set of training data and annotations, including
treating all foreground classes as one individual class (depending on the
largest component suppression increases in cross-validation performance)

Configuration and Training Process. We trained the nnU-Net framework using the
3D U-Net version with 5-fold cross-validation implemented on the training
sets. The evaluation results presented in the following section are the
averages on testing sets of 5-fold cross-validation. We used the nn-UNet
default auto-generated hyper-parameters for training, which include the
learning rate chen2017deeplab  initialised as 0.01 with a polynominal decay
policy of $(1-epoch/epoch_{max})^{0.9}$, the loss function as the sum of
cross-entropy and Dice loss drozdzal2016importance , an optimizer based on
stochastic gradient descent (SGD) with Nesterov momentum value of 0.99 and
epoch number of 1000 with mini-batches of 250. Notably, the default nnU-Net
typically requires $>30$ million parameters for the training. The proposed
model was implemented in Python language111https://www.python.org using
Pytorch NEURIPS2019_9015 . All experiments were on a NVIDIA TITAN RTX 24GB
GPU.

Figure 7: Overview of the experiments together with a sample 3D nnU-Net
architecture

###  3.3 Evaluation

We chose to perform four experiments. In three experiments, we used two of the
kidneys as training data and one as test data. These aimed to investigate how
well the nnU-Net approach would generalise to a new dataset where the donor
and thus anatomy and sample prep may differ slightly; it also had the virtue
of testing how different-sized training datasets impacted nnU-Net training
output. The overview of the experiments can be seen in Figure 7.

The fourth experiment aimed to see how well nnU-Net could segment the
remainder of a dataset given a limited amount of labelled slices from the same
dataset. Due to the size of HiP-CT datasets, such a model still has utility as
segmenting vascular networks for entire organs is highly time-consuming
rahmani2023micro . In this section, we selected kidney 1 for our experimental
evaluation. Half of the kidney has been utilized as a training set (1138
slices), and the remaining half as a testing set (1140 slices).

The importance of evaluation metrics in automatic image processing with
machine learning cannot be emphasised enough, as they serve as a basis for
determining the choice or practical applicability of a method. However, much
of the research has been on creating novel image processing algorithms leaving
the critical issue of reliably and objectively evaluating the performance of
these algorithms largely unexplored maier2022metrics . Moreover, some of the
commonly used evaluation metrics do not always correlate with clinical
applicability alidoost2023model ; vaassen2020evaluation , and the specific
features of a biomedical problem may make certain metrics unsuitable, such as
when the Dice Similarity Coefficient (DSC) is utilised to evaluate extremely
small structures antonelli2022medical . As a result, carefully choosing the
right evaluation metric for a given problem becomes important for validating
and comparing the performance of image processing methods.

In vessel segmentation, class imbalance presents a significant challenge where
the pixel count for the foreground class (vasculature) is notably lesser than
that of the background class (non-vasculature).

Class imbalance can hinder effective network training, as most data points are
typically negative samples (or backgrounds) that often do not offer valuable
learning insights. Moreover, these negative samples can dominate the positive
ones (such as arteries) during the training process as loss values are
predominantly generated from the negative samples.

Table 6: Results of all four experiments done using nn-UNet. SD: Surface
Distance; NSD: Normalised Surface Dice; CLD: Centerline Dice; t: tolerance;
Sym: symmetric

Expt. | Train data | Test data | Dice | CLD | NSD (t=1) | NSD (t=0) | Avg. sym. SD
---|---|---|---|---|---|---|---
1 | Kidney 1,2 | Kidney 3 | 0.9410 | 0.8886 | 0.9651 | 0.7631 | 4.300
2 | Kidney 1,3 | Kidney 2 | 0.9523 | 0.8533 | 0.9518 | 0.7120 | 0.8639
3 | Kidney 2,3 | Kidney 1 | 0.8585 | 0.8228 | 0.8968 | 0.7132 | 2.9270
4 | Half of kidney 1 | Other half of kidney 1 | 0.9513 | 0.8631 | 0.9404 | 0.8549 | 2.1561

A combination of overlap-based and boundary-based metrics is selected to
evaluate different properties of the model predictions maier2022metrics . For
overlap-based metrics, Dice Similarity Coefficient (DSC or Dice) is computed
and is complemented by the Centerline Dice (clDice) shit2021cldice  due to the
tubular nature of blood vessels and the importance of connectivity for
vascular structures. Overlap-based metrics have certain limitations such as
shape unawareness and inaccurate assessment when dealing with small structures
hence, boundary-based metrics, specifically Normalised Surface Dice (NSD) and
Average Symmetric Surface Distance (ASSD), are also computed using MONAI
v1.2.0 cardoso2022monai . The results of the four experiments are provided in
Table 6 and representative images for each kidney shown in Figure 8.

Figure 8: A sample 2D slice from each kidney together with their corresponding
ground truth labels and the output of the model segmentations (for kideny 1
the segmentation is provided by Expt. 3

##  4 Discussion

In this study, we evaluated model performance for the segmentation of the
arterial network from HiP-CT images of whole human kidney. Five metrics: Dice,
clDice, NSD (t=1), NSD (t=0), and ASSD, with results presented in Table 6 were
used to evaluate the outputs. The range of metrics we employed in our
experiments provides a thorough overview of model performance and allows the
state-of-the-art presented to be rigorously benchmarked against future
research. For each experiment, one unseen kidney is selected as test data for
evaluation of the model’s generalisation capability. The 5 metrics are
calculated on test data after the model training is finished. Beyond
quantitative evaluations, we provided visual insights through 3D
representations of the ground truth, model predictions, and false negatives.

Based on our literature review (see Tables 1, 2, 3, and 4), it is clear that
the Dice Similarity Coefficient (DSC), also known as Dice, is the predominant
metric for evaluating vascular segmentation model performance. Therefore,
employing DSC for result evaluation is essential to align with existing
literature, such as in hilbert2020brave . This metric quantifies the overlap
between segmentation predictions and ground truth. In our three experiments,
the first and second experiments yielded superior segmentation results on the
unseen kidney, with Dice scores of 0.9523 and 0.9410, respectively, whereas
the third experiment attained only 0.8585. 3D examination of kidney 1, as show
in Figure 9; revealed more collapsed vessels compared to kidney 1 and 3,
potentially explaining the lower Dice score when the second kidney was the
test subject. Nevertheless, DSC primarily assesses voxel-to-voxel concordance,
overlooking several crucial characteristics of the vessels. Hence, it should
not be the sole metric for deciding the performance of vascular segmentation.

Figure 9: The visualization of kidney 1 and model’s false negative prediction
together with subvolumes taken from it indicating the regions where model did
perform worst

Given the nature of vasculature networks - heirarchial structures of thin and
elongated tubular structures, ensuring sensitivity to small vessels and
mitigating boundary ambiguities is pivotal. Therefore, we also reported the
distance measurement by a distance metric known as ASSD (see equation 15) to
evaluate the boundary distances between predictions and ground truths. On top
of ASSD, we also calculated the NSD to evaluate the similarity of the surfaces
(or boundaries) of the segmented regions rather than the volumetric overlap.
Instead of considering the overlap of the entire segmented regions, NSD
assesses the closeness of their borders. It is particularly useful in
scenarios where small boundary deviations can be critical, such as in
segmenting thin structures or lesions. Tolerance ($t$) is an important hyper-
parameter when it comes to NSD, which relates to the acceptable amount of
deviation in the segmentation boundary in pixels. If a point on the boundary
of the prediction is within this tolerance distance from a point on the
boundary of the ground truth, it is considered a TP. Therefore, an appropriate
NSD tolerance should usually be selected based on inter-annotator variability
or some other heuristic. In our experiment, we initially set tolerance to a
strict 0 pixels and relaxed it to 1 pixel afterwards, with the latter
achieving around 25% increase in the NSD score. This shows that the model
fails to predict the exact boundaries of the vasculature, but 1-pixel error
tolerance can highly increase the segmentation results, which indicates that
while a post-processing step could be used to fix the larger vessels, the
vessels that might be only 1 pixel in diameter (as is the case in our dataset)
would still be missed. Consequently, models that improve segmentation of such
thin vessels are needed.

On the other hand, capturing vessel continuity and centerline of the structure
is vital even if the overall vessel shape is largely captured. To this end, we
applied clDice (see equation 7), a modified Dice, as another evaluation metric
to our experiment. Minor deviations or inaccuracies in the centerline of
vasculature structures can lead to a significant reduction of the clDice
score, as it treats larger as well as thinner structures equally, thereby
reducing bias toward algorithms that predict larger structures such as large
arteries but miss microvasculature structures. A centerline-based metric can
also provide a more consistent basis for comparison of vasculature continuity
as it reduces the influence of boundary details which might be more
susceptible to variations in resolution. In our case, discontinuities on the
boundaries (see Figure 9) and a decrease in performance capturing smaller
vessels at the end can result in lower clDice than standard Dice reported in
Table 6. Moreover, if the model underestimates or overestimates the width of
the vessels the standard DSC might still be relatively high because it
considers the overlap of the entire segmented region with the ground truth.
However, these inaccuracies can disrupt the vessel’s centerline, leading to a
lower clDice score. It should be noted that since clDice depends on the
underlying skeletonization algorithm, it should be carefully used, as
inefficiencies or method chosen for skeletonisation can lead to inaccurate
computation of the clDice score.

In Section 3.3, we describe an additional experiment (experiment four) to
evaluate the nnU-Net’s performance with a limited dataset. In this case we
used just kidney 1. We trained the model on half the data and tested it on the
remaining half. This experiment’s training-test division was designed to test
the model’s capabilities, to extrapolate from a small subset of images within
a single organ to the entire dataset. This reduces the challenge of the
segmentation task but also reduces the utility of the final model, however
given the size of HiP-CT datasets, such an approach is likely to still have
utility in particularly challenging or anatomically unique datasets. The
performance metrics for the test set were Dice = 0.9513, clDice = 0.8631, NSD
(t=1) = 0.9404, NSD (t=0) = 0.8549, and ASSD = 2.1561. These results indicate
that nnU-Net is effective at segmenting ’unseen’ kidney tissues from the same
organ, reflecting its capability to learn kidney anatomy and thereby enhancing
the efficiency of the labeling process. It also shows the expected improvement
by comparison to the other three experiments, indicating the challenges with
accounting for anatomical variation across human samples, or variations in the
data aquisition and data reconstruction parameters see brunet2023preparation ;
walsh2021imaging ; xian2022multiscale  for details on protocol variations. The
variation is also expected as the HiP-CT technique continues to develop and as
the Human Organ Atlas extends it would be possible to use automated
segmentation techniques to perform large studies to analyse vascular network
differences due to pathology, age or sex etc of the donor.

From the discussion on metrics above, the benchmarking methodology of nnUNet
fails in several scenarios when applied to HiP-CT data on vascular
segmentation. With whole organ HiP-CT we are able to manually segment and
create training data down to the level of the interlobular arteries or approx.
two generations from the capillary bed. These arteries have a minimum radius
of ca. 40$\mu{m}$ rahmani2023micro , (1-2 pixels in diameter). However, the
experiment scores reveal that the failed prediction on such small vessels
leads to discontinuity problems for the vasculature structure estimation. The
work in shit2021cldice  has shown an improved continuity performance on
vasculature segmentation with clDice as part of the loss function when
training the network. Integration of rich hierarchical representations of thin
and long structures by use of spatial attention and channels attention modules
also can potentially improve the outcomes mou2021cs2 . Another challenge that
may hinder vasculature continuity is edge identification and segmentation.
Considering the imbalance of edge and non-edge voxels, an edge-reinforced
neural network (ER-Net) xia20223d  combining a reverse edge attention module
zhang2020cerebrovascular  and an edge-enforced optimisation loss to discover
spatial information of the edge structures could potentially increase the
vasculature continuity on segmentation results. In both the small vessel and
edge enhancement cases it is important to remember the HiP-CT is a
propagation-based phase contrast technique and thus has intrinsic contrast for
small structures and edges between structures. Tomographic reconstruction is a
part of the process of HiP-CT data volume data production, and can have a
large impact on the ease of segmentation. Tuning the tomographic
reconstruction pipeline to enhance specific features, whilst challenging could
be a powerful approach to segmenting specific smaller structures.

Relative to the ground truth, the benchmark model appears to overlook certain
vasculature, particularly struggling to identify certain large vessels Figure
9. Upon closer examination of the pertinent sub-volumes, it becomes evident
that these misdetected large vessels correspond to collapsed vessels within
the organs. As shown in Figure 9 of the prediction on kidney 1, collapsed
vessels have a flat appearance, morphologically different from the
noncollapsed vessels seen elsewhere. This suggests that the model’s
performance falters notably in the presence of disrupted structures, and these
could be the potential cause for the observed lower scores when kidney 1 is
used as the test set.

Therefore, HiP-CT scans in high-resolution present challenges to deep learning
methods applied to different tasks such as segmentation. These scans highlight
more details that must be captured by the models. Using a larger patch size
for training can ease the problem. However, there is a trade-off between the
GPU memory and the size of the receptive field. As a result, integrating
regional features and their global dependencies remains a research direction.

##  5 Conclusion and future work

The vascular system is vital for nurturing and supporting every organ in the
body, and its abnormalities can be indicative of various diseases. Developing
accurate and automated segmentation of vasculature, that can be applied to
nasent imaging technologies such as HiP-CT, could pave the way for scalable
vascular segmentation across large-scale datasets. Scalability in turn enables
quantification across a large demographic population, therefore assisting the
construction of a data-driven Vascular Common Coordinate Framework
boppana2023anatomical ; borner2021anatomical  for human atlasing projects such
as the Human BioMolecular Atlas Program hubmap2019human , the Human Organ
Atlas Project walsh2021multiscale  and the Cellular Senescence Network Program
sennet2022nih .

This paper offers a comprehensive review of recent literature focusing on
deep-learning techniques for blood vessel segmentation. We primarily delve
into the recent deep-learning methodologies, emphasizing the challenges
associated with vasculature segmentation. Our aim with this review is to lay a
solid foundation for researchers, building robust models for vessel
segmentation, especially using phase contrast imaging.

For these models to be widely accepted in the medical field, we need to
address certain gaps in the current research. From our literature survey, we
deduce the following: (i) Publicly accessible datasets for vascular
segmentation are limited. Even within private datasets, the size available for
training is often limited. (ii) Labeling the vascular region remains a
formidable challenge and requires extensive effort. (iii) Most studies employ
private data and provide internal validation. This makes direct comparisons
between studies challenging and prevents the assessment of algorithm efficacy.
(iv) Every phase of the imaging process; from sample preparation, imaging,
modality specific artefact correction, pre-processing and algorithm
optimization to post-processing, holds immense importance. The post-processing
stage, especially, has a great impact on the final segmentation.

Many studies focus on mapping out complex blood vessel networks, mostly
concentrating on larger vessels yet the significance of smaller vessels, which
are frequently overlooked, cannot be understated from a (patho-)physiological
point of view ackermann2022bronchial ; ackermann2022fatal . Moreover, the
choice of evaluation metrics and loss functions in vessel segmentation is
often unclear, calling for clearer guidelines. Through our study, we aim to
assist researchers in identifying the most suitable metrics for their
analyses. One significant challenge with vessel segmentation models is their
limited generalisability, as most studies rely on private datasets. To enhance
adaptability, models should be cross-validated against diverse data. Providing
both more public datasets and pre-trained model weights could benefit future
research. By making our data publicly accessible, through a Kaggle competition
(https://www.kaggle.com/competitions/blood-vessel-segmentation/data) and
following its conclusion, through the human organ atlas portal (www.human-
organ-atlas.esrf.eu), we invite researchers to evaluate their algorithms on
HiP-CT data, fostering advancements in the field.

## Acknowledgement

The authors would like to express their gratitude to P. Masson (LADAF) for
dissections of body donors’, the Hannover Biobank for provision of donor
organs,Emer O’Leary for segmentation input, E. Boller, K, Dollman, C. Berruyer
and C. Jarnias for their help in setup, developments, improvements and
imaging. The authors would like to express gratitude for the financial support
provided by the Chan Zuckerberg Initiative DAF (2020-225394), an advised fund
of SVCF, the MRC (MR/R025673/1), and ESRF beamtimes (md1252 & md1290). P.D.L.
is supported by a Royal Academy of Engineering Chair in Emerging Technologies
(CiET1819/10). This research was funded in part by the Wellcome Trust
[209553/Z/17/Z]. The grant of the European Research Council (ERC); European
Consolidator Grant, XHale to Danny Jonigk (ref. no.771883). This work was
supported by the German Registry of COVID-19 Autopsies (DeRegCOVID,
www.DeRegCOVID.ukaachen.de; supported by the Federal Ministry of
Health—ZMVI1-2520COR201), and the Federal Ministry of Education and Research
as part of the Network of University Medicine (DEFEAT PANDEMIcs, 01KX2021).

This research has been funded by the NIH Common Fund through the Office of
Strategic Coordination/Office of the NIH Director under awards OT2OD033756 and
OT2OD026671, by the Cellular Senescence Network (SenNet) Consortium through
the Consortium Organization and Data Coordinating Center (CODCC) under award
number U24CA268108, by the Kidney Precision Medicine Project grant
U2CDK114886, by the NIDDK under awards U24DK135157 and U01DK133090 and by The
Multiscale Human CIFAR project. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the
manuscript.

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