# SHARM: Segmented Head Anatomical Reference Models

Essam A. Rashed<EMAIL_ADDRESS>Mohammad Al-Shatouri  Ilkka Laakso
Akimasa Hirata  Graduate School of Information Science, University of Hyogo,
Kobe 650-0047, Japan  Radiology Department, Faculty of Medicine, Suez Canal
University, Ismailia 41522, Egypt  Department of Electrical Engineering and
Automation, Aalto University, Espoo, Finland  Aalto neuroimaging, Aalto
University, Espoo, Finland  Department of Electrical and Mechanical
Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan  Center of
Biomedical Physics and Information Technology, Nagoya Institute of Technology,
Nagoya 466-8555, Japan

###### Abstract

Reliable segmentation of anatomical tissues of human head is a major step in
several clinical applications such as brain mapping, surgery planning and
associated computational simulation studies. Segmentation is based on
identifying different anatomical structures through labeling different tissues
through medical imaging modalities. The segmentation of brain structures is
commonly feasible with several remarkable contributions mainly for medical
perspective; however, non-brain tissues are of less interest due to anatomical
complexity and difficulties to be observed using standard medical imaging
protocols. The lack of whole head segmentation methods and unavailability of
large human head segmented datasets limiting the variability studies,
especially in the computational evaluation of electrical brain stimulation
(neuromodulation), human protection from electromagnetic field, and
electroencephalography where non-brain tissues are of great importance.

To fill this gap, this study provides an open-access Segmented Head Anatomical
Reference Models (SHARM) that consists of 196 subjects. These models are
segmented into 15 different tissues; skin, fat, muscle, skull cancellous bone,
skull cortical bone, brain white matter, brain gray matter, cerebellum white
matter, cerebellum gray matter, cerebrospinal fluid, dura, vitreous humor,
lens, mucous tissue and blood vessels. The segmented head models are generated
using open-access IXI MRI dataset through convolutional neural network
structure named ForkNet+. Results indicate a high consistency in statistical
characteristics of different tissue distribution in age scale with real
measurements. SHARM is expected to be a useful benchmark not only for
electromagnetic dosimetry studies but also for different human head
segmentation applications.

###### keywords:

Human head models, brain segmentation, convolutional neural networks, MRI

††journal: NeuroImage

##  1 Introduction

Anatomical reference models of human subjects are of great importance in
several computer simulation studies such as medical imaging, dosimetric
evaluation for diagnosis and therapy and human safety. In principle, digital
models are generated from anatomical imaging of real subjects for better
understanding real physical effects. Specifically, personalized electrode
positions or coil location are explored in the non-invasive electrical and
magnetic stimulation (Antonenko et al., 2019), in addition to group-level
optimization(Laakso et al., 2015; Gomez-Tames et al., 2018). Variability
analysis is needed to derive the limit in human protection from
electromagnetic field (Hirata et al., 2021; ICNIRP et al., 2020).

Several attempts provided different models that represent whole body models
(Nagaoka et al., 2004; Kim et al., 2008; Christ et al., 2009; Segars et al.,
2010; Yu et al., 2015). A useful review is in (Kainz et al., 2019).
Segmentation of brain tissues is of high interest in several clinical
applications such as diagnosis of abnormalities, assessment of
neurophysiological performance, surgery planing and many others. Most of
standard medical imaging applications can represent brain tissues in high
contrast which enable accurate automatic annotation (Baur et al., 2021).
However, segmentation of non-brain tissues is challenging as it represented in
low contrast and/or allocated in limited regions. Moreover, in clinical
medical applications, imaging protocols are usually adjusted such that brain
tissues are presented in high quality as the main target of diagnostic
applications (Kalavathi and Prasath, 2016).

Whole head segmentation have been discussed mainly for the development of
digital models for electromagnetic stimulation studies. SimNibs is an open
source software for the simulation of non-invasive brain stimulation that
include magnetic resonance (MR) image segmentation to generate head models
(Thielscher et al., 2015; Saturnino et al., 2018). However, segmentation is
limited to major head tissues such as white matter (WM), grey matter (GM),
cerebrospinal fluid (CSF), skull and scalp. ROAST is another pipeline the
include automatic MRI segmentation based on SPM12 (Ashburner and Friston,
2005) with variety of segmentation and electromagnetic modeling options (Huang
et al., 2019). However, ROAST segmentation is also limited to a few number of
tissues as in Table 1 in Ref. (Huang et al., 2019). Segmentation of fifteen
head tissues using multi-modality images (MRI T1/T2, mDixon, venogram and CT)
is proposed in (Puonti et al., 2020). Recently, the use of deep learning
architectures demonstrate quality improvement of anatomical segmentation
(Akkus et al., 2017). Several network architectures such as ForkNet (Rashed et
al., 2019), SubForkNet (Rashed et al., 2020) and FastSurfer (Henschel et al.,
2020) have been used to generate human head models with different scope and
applications.

Due to the complexity of full head segmentation and requirements of intensive
efforts for manual parameters adjustment, there is a shortage of relatively
large dataset of human head models. This problem becomes more feasible with
the use of deep learning as robust segmentation tool with superior accuracy
compared to conventional methods. The aim of this work is to generate an open-
access Segmented Head Anatomical Reference Models (SHARM) that is large enough
for subject variability studies. The developed dataset consists of 196
subjects segmented into 15 different tissues. The main contributions of this
study can be summarized as follows:

  * 1.

An open-source deep learning pipeline for automatic segmentation of MRI head
images.

  * 2.

An open-access large human head dataset segmented into brain and non-brain
tissues.

  * 3.

Evaluation of the consistency of segmented models with realistic tissue
characteristics.

##  2 Materials and methods

###  2.1 Dataset and general pipeline

The MRI dataset used in this study is the IXI Dataset111http://brain-
development.org/ixi-dataset/ which consists of around 600 MRI scans of healthy
subjects. A set of 196 subjects are selected (123 females, 70 males, and 3
unknown), that are imaged at two hospitals (100 were imaged at the Guy’s
Hospital (London, UK) with Philips 1.5T system and 96 were images at the
Hammersmith Hospital (London, UK) using a Philips 3T system). Excluded images
criteria are based on quality of the image and availability of multi-modlity
scans. The T1w/T2w image data are in Nifti formats and are used for generation
of the head models.

The raw T1 and T2-weighted MR images are registered using non-rigid
registration such that T2w are adjusted to fit with T1w. The acoustic noise is
reduced through contouring of head surface and relabeling external region as
air voxels. The N4 bias field correction method (ITK222https://itk.org/) is
used for bias correction of both MRI modalities. Both T1w/T1w images are
normalized with zero mean and unit variance, then scaled to vales [0.01,
0.99]. All the above pre-processing procedures are used to generate the
network input volumes (a set of two 2563 volumes representing T1w/T2w MRI with
unified 13mm resolution). A selected set of the network input is segmented
into 15 different head tissues using the semi-automatic method detailed in
(Laakso et al., 2015). The segmentation binary labels are used as network
target (output) through training process. The remaining subjects are evaluated
using trained network to automatically generate segmentation labels. Finally,
an aggregation process is used to combine different segmentation labels into a
head model. The pre-processed MR scans and segmented head models are available
for each subject in SHARM dataset. The data processing pipeline is shown in
Fig. 1.

Figure 1: Data flow used to generate SHARM from IXI dataset. Figure 2:
ForkNet+ with MRI T1w/T2w inputs and $N$ segmented tissues outputs.

###  2.2 Semi-automatic segmentation

The target dataset for the training process was generated using a semi-
automatic segmentation pipeline that segments T1- and T2-weighted MR image
data into 15 tissue types (Laakso et al., 2015). Briefly, after bias
correction and normalization of the MR data, the pipeline first splits the
head into three compartments: inner compartment, consisting of the volume
inside the inner surface of the skull; middle compartment, consisting of the
skull and nasal cavity; and the outer compartment, consisting of the volume
between the outer surface of the skull and the outer surface of the skin. The
quality of these compartments is verified by visual inspection, and whenever
necessary, control parameters are manually altered until the compartments
match the MR data.

The inner compartment is segmented into brain using FreeSurfer image analysis
software (Dale et al., 1999; Fischl and Dale, 2000). The brain segmentation
consists of cerebral gray matter, cerebral white matter, cerebellar gray
matter, cerebellar white matter, deep brain structures (brainstem, accumbens,
amygdala, caudate, hippocampus, pallidum, putamen, thalamus), and ventricular
CSF. The remaining non-brain volume in the inner compartment is segmented into
CSF (bright T2-weighted image), blood (dark T2), and dura (non-brain non-CSF
tissue close to the inner boundary of the skull). Anterior and middle cerebral
arteries initially estimated from T2 are corrected using thresholding of
registered MRA images (when available). Deep brain structures are treated as
GM. The middle compartment consisting of the skull is segmented into cortical
and cancellous bone by thresholding the T2-weighted MRI data. It is ensured
that the inner and outer cortical bone layers are at least 1 mm and 1.5 mm
thick, respectively. The nasal cavity also belongs to the middle compartment
and is segmented as either mucous tissue or cortical bone based on T2-weighted
images. The outer compartment is segmented into skin, fat, muscle, and eyes.
The scalp (including subcutaneous fat) is segmented as the outer layer of the
head, with thickness between 2 mm and 10 mm. Fat and muscle are segmented
based on thresholding the T1-weighted image data. Finally, eyes and lens are
segmented using both T1- and T2-weighted image data.

The resulting segmentation has uniform voxel size of 0.5 mm$\times$0.5
mm$\times$0.5 mm, half of that of the input MR images. In this study, the
segmented dataset was downscaled to the same resolution as the input images
using three-dimensional nearest neighborhood interpolation algorithm.

###  2.3 Network architecture

The deep learning architecture used here is an extension of ForkNet (Rashed et
al., 2019) by considering input data from both T1- and T2-weighted MRI scans.
We then refer to the new network as ForkNet+. The network inputs are the MRI
scans in two encoders and outputs are $N$ decoders each assigned to single
anatomical structure (here $N$=15). The details of the layer structures and
data processing flow is shown in Fig. 2. The network output are binary masks
that identify different anatomical tissues/liquids such as skin, muscle, fat,
skull (cortical bone), skull (cancellous bone), CSF, blood vessels, dura,
brain GM, brain WM, cerebellum GM, cerebellum WM, vitreous humor, eye lens,
mocous tissue and whole head. ForkNet+ design is flexible and easy to be
adjusted to segmented specific number of tissues for different applications as
each tissue is segmented using separate decoder (Fig. 2).

Figure 3: Network evaluation through different direction to generate the head
model.

###  2.4 Head model generation

Once the network is well-trained, the head models are generated through fast
evaluation process. To reduce artifacts caused by 2D slice segmentation, a set
of three networks are trained using slices of axial, sagittal and coronal
directions as shown in Fig. 3. A rule-based segmentation merge approach using
majority vote is used to generate the final segmentation from different
slicing directions. When no majority in a voxel is found, the neighborhood
majority vote (Rashed et al., 2020).

Figure 4: Volume rendering sample of generated head models. Figure 5: Axial,
sagittal and coronal slices (top to bottom) of head models shown in Fig. 4 in
order.

##  3 Results

A set of 20 randomly selected head models and associated segmented labels are
used to train ForkNet+. The network architecture is developed using Wolfram
Mathematica (R) ver. 13.0, installed on a Ubuntu 20.04 workstation of 12 Cores
Intel (R) Core (TM) i9-10920X @3.50GHz, 64 GB memory, and NVIDIA RTX A6000
GPU. A three networks (axial, sagittal and coronal) are trained with cross-
entropy loss function and ADAM optimization algorithm. The training was
considered using 50 epochs with batch size 4. To reduce the computation cost,
the number of output tracks is set to $N=4$ (i.e., a set of 4 tissues are
trained simultaneously). A single training round requires about 24 mins.

The remaining 176 head models are evaluated through trained networks and the
network output is aggregated to generate the head models. Example of generated
head models are shown in Figs. 4 and 5. In some few cases, some manual edition
is required mainly to remove a small regions of CSF-like tissue uncorrected
segmented inside the mouth. Some other cases are excluded due to the strong
noise that is difficult to be automatically removed and lead to incorrect
segmentation of external contour. Evaluation of segmentation accuracy is not
conducted due to the lack of manual true annotation and it is out of the scope
of this work. However, we provide quantitative assessment of different tissues
of the SHARM dataset. In principle, we study the variability of segmented
volumes within age scale to validate the validity of SHARM models.

Figure 6 demonstrates a regression curves of segmented brains. Brain is
considered as a composition of GM, WM and CSF. It shows that a decline in
global GM volume with age ($R^{2}$=0.274), while there is no significant
change in WM volume with age ($R^{2}$=0.001). A remarkable increase in CSF
volume with age ($R^{2}$=0.277). Difference between gender grouping and
percentage of different structures with respect to total intracranial volume
(TIV) are also shown in Fig. 6 and it is consistent with results reported in
literature (e.g., (Ruigrok et al., 2014; Ge et al., 2002; Good et al., 2001)).
The change in TIV over age and the correlation between GM/WM ratio with age is
shown in Fig. 7. The change of skin, muscle, fat, skull, vitreous humor, and
eye lens volumes is shown in Fig. 8. In general the volume of skin, muscles,
and fat tissues are increasing with aging. Skull and eye lens does not change
so much while the volume of vitreous humor is shrinked as reported earlier
(Sebag, 1987). Data shown in Fig. 9 demonstrate that the volume of brain is
highly correlated with the body mass index (BMI) and brain volume of male
subjects are of large volume compared with females (Eliot et al., 2021). SHARM
dataset can be download333https://figshare.com/s/a4d9ba6f18a6b7f7ba2c (zip,
7.62 GB) in MATLAB (*.mat) files with structure shown in Fig. 10.

Figure 6: (a) Regression lines with scatter plot of total GM volume over age
for all subjects (female in orange and male in green). (b) and (c) are
regression lines and scatter plots for total WM and CSF, respectively. (d)-(f)
are the regression lines and scatter plots for fractional volume (with respect
to TIV) of GM, WM and CSF, respectively. Figure 7: (a) Regression curves and
scatter plot of (a) total intracranial volume (TIV) in liters and (b) GM/WM
ratio over age for all subjects.

##  4 Discussion

This work demonstrate a new benchmark dataset for several computational
neuroscience applications. The open-access SHARM consists of 196 head models
segmented into 15 different tissues that cover a wide range of subject
variability. A boxplot demonstrates the volume variations of different
structures in SHRAM is shown in Fig. 11. It is clearly observed that skull
volume is 0.827 $\pm$ 0.08 $L$ (male) and 0.730 $\pm$ 0.08 $L$ (female). These
values are highly correlated with those listed in the ICRP Reference man
(averaged bone without marrow of Skull is 708 gm). The vitreous humor is
15.098 $\pm$ 2.10 $mL$ (male) and 14.124 $\pm$ 1.91 $mL$ (female) that is
referenced as value 15 $\pm$ 6.5 gm in adult (ICRP Publication 23, 1975, Table
97, p. 220). Also, eye lens is 0.246 $\pm$ 0.07 mL (male) and 0.242 $\pm$ 0.08
mL (female) which is calculated as 172 to 258.1 gm for (20 - 60 y) adult (ICRP
Publication 23, 1975, Table 100, p. 225). Calculated volume and weight of
different SHRAM tissues are compared with those reported in (ICRP Publication
23, 1975) in Table 1. These values are example that indicate segmentation
accuracy of non-brain tissues.

Table 1: Comparison of volume/weight values of SHARM models with ICRP
Reference man (ICRP Publication 23, 1975).

_∗ Average density values are acquired from (IT’s Foundation, 2023)_
---
_∗∗ References from (ICRP Publication 23, 1975)_

The segmented models along with normalized T1- and T2-weighted MR scans are
available for each subject in additional to other demographic information.
Most of the models in SHARM are presented with full head and neck segmentation
which enables simulation studies that requires full head models. moreover, the
trained deep learning model used to generate SHARM is shared which can be used
to generate additional models considering the availability of consistent MR
scans. The software ForkNet+ generate individual tissue segmentation in terms
of probability maps which enables customized segmentation of a single subject
through weighting based aggregation process (similar to those presented in
(Rashed et al., 2021)). It is worth noting that evaluation of segmentation
accuracy is out of the scope of this work, because earlier version of the
ForkNet (Rashed et al., 2019) segmentation have been evaluated.

Figure 8: Regression curves of segmented volume of (a) skin, (b) muscle, (c)
fat, (d) skull, (e) vitreous humor, and (f) eye lens in SHARM models.

The limitation of this work is the lack of variability of MR data acquisition.
Data are acquired from two scanners installed at two medical institutes but it
is developed by the same manufacturer. Further extension with data from other
manufacturers are planned to be included in future versions. Moreover, we will
include more information considering the segmentation of deep brain structures
and fiber orientations in future versions of SHARM. Also, there is a lack of
bone structure accuracy in the neck region as lack of neck data in T2 images.
In future, we will investigate potential approaches to improve the
segmentation neck region properly.

Figure 9: Regression curves of TIV per BMI.

##  5 Conclusion

In this study, we present SHARM, a benchmark dataset of 196 segmented human
head models. The models are segmented into 15 different tissues using deep
learning network named ForkNet+. The freely available models along with
normalized MR T1- and T2-weighted scans would enable a large scale studies in
different applications such as electromagnetic brain stimulations. Results
demonstrate that the segmented models are of high consistency with
measurements obtained from real measurements. One feature of ForkNet+ is the
segmentation of each tissue is generated as probability maps that enable
parametric segmentation for further customization of the generated head
models. The trained networks as well as source code are shared for potential
usage of head models generation. With large scale of subject age, SHARM would
enable different electromagnetic dosimetry and human safety studies in a
reliable manner. After publication, Mathematica notebooks demonstrate the
implementation of ForkNet+ architectures and trained networks will be
available for download at: https://github.com/erashed/ForkNetPlus

Figure 10: Structure of subject SHARM076 that include MRI scans, head model
and other subject details. Figure 11: Boxplot demonstrates volume variations
of different structures in SHARM male (top) and female (bottom) subjects.

## Acknowledgment

This work was funded by the Japan Society for the Promotion of Science (JSPS),
a Grant-in-Aid for Scientific Research, Grant number JSPS KAKENHI 22K12765.

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