5.2.1), and other methods for tumors (Section 5.2.2), cysts (Section 5.3),
inflammation (Section 5.4), semi-supervised learning (Section 5.5),
generalization to other datasets (Section 5.6), and design of new loss
functions (Section 5.7). As for parenchyma, this section ends by comparing the
performances of the different DL models (Section 5.8).

Figure 12: Spatial distribution and frequency of pancreas tumors within the
MSD dataset with 281 cases and case $\\#$29 as a reference in the image
(Simpson et al. (2019)): most frequent pancreases in the dataset in red, least
frequent ones in blue. Boundary of case $\\#$29 in white.

###  5.1 Variability of tumors size and location

Following a similar approach described for parenchyma segmentation (cfr.
Section 4), a registration was performed on 281 CTs of the MSD dataset, with
case $\\#$29 as reference. The result is depicted in Fig. 12, showing the
broad spatial distribution and frequency of pancreas tumors within the MSD
dataset.

Table 7:  Studies on tumors and cysts.

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main Contributions Cao and Li (2024) Parenchyma and tumors 82 (NIH)
281 (MSD) UNet with: High resolution spatial information recovery + Multi-
scale high resolution pre-segmented feature fusion + Pyramid multi-scale
feature perception and fusion Supervised Difficulty- guided adaptive boundary-
aware loss Parenchyma (NIH): 88.96% (DSC) 89.27% (Precision) 89.98% (Recall)
Parenchyma (MSD): 89.52% (DSC) 93.19% (Precision) 88.71% (Recall) Tumors
(MSD): 54.38% (DSC) 69.58% (Precision) 53.17% (Recall) High-resolution spatial
information recovery module: encoder and decoder features of the same layer
are sent to high resolution spatial information filtering module to extract
high-resolution pre-segmented images, which are then fused. Multi-scale high-
resolution pre-segmented feature fusion module: features of the encoder and
decoder finely processed into a high-resolution pre-segmented feature map.
Pyramid multi-scale feature perception and fusion module uses the extracted
pre-segmented images to guide the network to focus on the dimensional changes
of the segmented targets. Design of Difficulty-guided adaptive boundary-aware
loss function to address the class imbalance and improve segmentation of
uncertain boundaries Cao et al. (2023b) Parenchyma and tumors 82 (NIH) 420
(MSD) UNet with three attention mechanisms on skip connections: Spatial +
Channel + Multi-dimensional features Supervised Weighted cross entropy loss
Parenchyma (NIH): 83.04% (DSC) 81.71% (Precision) 84.42% (Recall) Parenchyma
(MSD): 83.39% (DSC) 85.51% (Precision) 81.37% (Recall) Tumors (MSD): 40.15%
(DSC) 52.32% (Precision) 35.29% (Recall) Design of a loss function to capture
edge details of pancreas and tumors. Multi-dimensional attention gate
integrated into skip connections for small target feature localization in
multiple dimensions of space and channels, and for filtering redundant
information in shallow feature maps, thus enhancing the feature representation
of the pancreas and pancreatic tumor Du et al. (2023) Pancreas ductal
adenocarcinoma 55 (Qingdao University Hospital) 281 (MSD) UNet with multi-
scale channel attention Supervised Binary cross entropy Qingdao: 68.03% (DSC)
59.31% (Jaccard) 12.04 mm (HD) MSD: 80.12% (DSC) 74.17 (Jaccard) 2.26 mm (HD)
Integration of multi-scale convolutions and channel attention into each
encoder and decoder block He and Xu (2023) Parenchyma and tumors 420 (MSD) +
dataset of other organs Hybrid CNN-Transformer Encoder: (3D Swin-Transformer +
boundary extracting module) + Boundary preserving module + Decoder: CNN
Supervised Dice loss Cross entropy loss Parenchyma: 81.47% (DSC) 1.77 mm
(ASSD) Tumor: 51.83% (DSC) 17.13 mm (ASSD) Application of boundary awareness
into 3D CNN and transformers. Swin-transformer as encoder and auxiliary
boundary extracting module to obtain rich and discriminative feature
representations. Boundary preserving module to fuse boundary map and features
from the encoder Ju et al. (2023) Parenchyma and tumors 82 (NIH) 281 (MSD)
UNet: Spatial visual cue fusion + Active localization offset (Localization)
UNet (Segmentation) Supervised Dice loss Binary cross entropy loss Parenchyma
(NIH): 85.15% (DSC) Tumor (MSD): 63.36% (DSC) Spatial visual cue fusion, based
on conditional random field, learns global spatial context. It combines the
correlations between all pixels in the image to optimize the rough and
uncertain pixel prediction during the coarse stage. Active localization offset
adjusts dynamically the localization results during the coarse stage. Code
available at https://github.com/ PinkGhost0812/SANet

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main Contributions Li et al. (2023e) Parenchyma and tumors 281 (MSD)
nnUNet with attention + Balance temperature loss + Rigid temperature optimizer
+ Soft temperature indicator Supervised Balance temperature loss Parenchyma:
85.06% (DSC) Tumors: 59.16% (DSC) Segmentation of both pancreas and tumors.
Balance temperature loss to dynamically adjust weights between tumors and the
pancreas. Rigid temperature optimizer to avoid local optima. Soft temperature
indicator to optimize the learning rate Mukherjee et al. (2023) Pancreas
ductal adenocarcinoma 1,151: Mayo Clinic + 152 from MSD and 41 from TCIA 3D
nnUNet Supervised Dice loss Cross entropy loss Overall: 84.00% (DSC) 4.6 mm
(HD) Generalization on MSD: 82.00% (DSC) 2.6 mm (HD) Generalization on TCIA:
84.00% (DSC) 4.30 mm (HD) Bounding boxes by cropping the CT images to a 3D
bounding box centered around the tumor mask. nnUNet applied to bounding boxes
Ni et al. (2023) Recurrence of pancreas ductal adenocarcinoma after surgery
205 (Internal) 64 (For recurrence prediction with radiomics) AX-UNet with
Atrous spatial pyramid pooling Supervised – 85.90% (DSC) 74.20% (Jaccard)
89.70% (Precision) 87.60% (Recall) AX-UNet combining UNet and atrous spatial
pyramid pooling. Code available at: github.com/ zhangyuhong02/AX-Unet Qu et
al. (2023) Parenchyma and masses (tumors, cysts) 313 (Peking Union Medical
College Hospital) 53 (Guandong General Hospital) (generalization) 50 (Jingling
Hospital) (generalization) MSD (420) (generalization) Swin Transformer and 3D
CNN (Based on M3NET) Feature alignment: Transformer guided fusion + Cross-
network attention (Decoder) Supervised Weighted cross entropy loss Pancreas:
Peking: 92.51% (DSC) Guangdong: 89.56% (DSC) Jingling: 88.07% (DSC) MSD:
85.71% (DSC) Masses: Peking: 80.51% (DSC) Guangdong: 67.17% (DSC) Jingling:
69.25% (DSC) MSD: 43.86% (DSC) CNN and transformer branches perform separate
feature extraction in the encoder. Progressive fusion between CNN and
transformer in the decoder. Transformer guidance flow to address the
inconsistency of the feature resolution and channel numbers between the CNN
and transformer branches. Cross network attention into CNN decoder to enhance
fusion capability with the transformer Wang et al. (2023) Tumors 93 (Shanghai
Changhai Hospital) 3D UNet-like: Encoder: Multi-modal fusion downsampling
block Decoder: Multi-modal mutual calibration block using attention Supervised
Dice loss 76.20% (DSC) 63.08% (Jaccard) 6.84 mm (HD) 75.96% (Precision) 84.26%
(Recall) Multi-modal fusion downsampling block to fuse semantic information
from PET and CT, and to preserve unique features of different modal images.
Multi-modal mutual calibration block to calibrate different scale semantics of
one modal images guided by attention maps from the other modal images Zhou et
al. (2023) Tumor 116 abnormal pancreas 42 normal pancreas (internal) Dual
branch encoder-decoder (Pancreas segmentation) Encoder-decoder: contrast
enhancement block + reverse attention block (Tumor segmentation) Supervised
Dice loss Abnormal: 78.72% (Jaccard) 89.07% (Precision) 87.42% (Recall)
Normal: 87.74% (Jaccard) 91.47% (Precision) 95.50% (Recall) Dual branch
encoder combining semantic information extraction and detailed information
extraction. Aggregation of feature maps of the two branches. Decoder to
segment pancreas. Enhancement encoder-decoder network to improve segmentation
accuracy of pancreatic tumors. Contrast enhancement block after each encoding
step to extract the edge detail information. Reverse attention block inverting
the decoder feature to guide the extraction of effective information in the
encoder to generate an accurate prediction map

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main Contributions Zou et al. (2023) Dilated pancreatic duct 150
(Internal) Nanjing Drum Tower Hospital 40: Jiangsu Province Hospital of
Chinese Medicine (Generalization) 3D nnUNet for: (Localization) Terminal
anatomy attention module (Segmentation) Terminal distraction attention module
(Refine stage) Supervised Terminal Dice loss Internal: 84.17% (DSC) 11.11 mm
(HD) Generalization: 82.58% (DSC) First work on errors on terminal regions of
the dilated pancreatic duct. Terminal anatomy attention module to learn the
local intensity from the terminal CT images, feature cues from the coarse
predictions, and global anatomy information. Terminal distraction attention
module to reduce false positive and false negative cases. Design of terminal
Dice loss for segmentation of tubular structures Li et al. (2023g) Pancreatic
cysts 107 (internal) UNet with: Atrous pyramid attention module + Spatial
pyramid pooling module Supervised Dice loss Binary cross entropy loss 84.53%
(DSC) 75.81% (Jaccard) Atrous pyramid attention module and spatial pyramid
pooling module inserted into bottleneck layer to extract features at different
scales, and contextual spatial information, respectively Duh et al. (2023)
Pancreatic cysts 335 (Internal) Spain UNet with Attention gate in skip
connections Supervised Dice loss 93.10% (Recall) Attention gate integrated
into skip connections for segmentation of pancreatic cysts Li et al. (2022b)
Tumors 163 (Shanghai Jiao Tong University) 468 MRI (for style transfer) 281
(MSD) (generalization) CycleGAN-like for: Synthetic data from MRI (Style
transfer) ResNet: Extraction of knowledge from MRI (Meta-learning I) +
Integration with salient knowledge from CT (Meta-learning II) Supervised
Adversarial loss Cycle consistency loss Dice loss Shanghai Jiao Tong
University: 64.12% (DSC) MSD: 57.62% (DSC) First study on meta-learning from
one to a different modality. Random style transfer on MRI: generation of
synthetic images with continuously intermediate styles between MRI and CT to
simulate domain shift. First meta-learning: the model learns the common
knowledge of synthetic data, and provides pancreatic cancer-related prior
knowledge for the target segmentation task. Second meta-learning: the model
learns the salient knowledge of the CT data to enhance segmentation Mahmoudi
et al. (2022) Tumors and surrounding vessels 138 (MSD) 3D local binary pattern
(Localization) Ensemble of: Attention gate + Texture Attention block (Scale
invariant feature transform and local binary pattern) (Segmentation)
Supervised Generalized Dice loss Weighted Pixel-wise Cross entropy loss
Boundary loss Tumor: 60.60% (DSC) 3.73 mm (HD95) 57.80% (Precision) 78.00%
(Recall) Superior mesenteric artery: 81.0% (DSC) 2.89 mm (HD95) 76.00%
(Precision) 87.00% (Recall) Superior mesenteric vein: 73.00% (DSC) 3.45 mm
(HD95) 68.00% (Precision) 81.00% (Recall) Design of texture attention block
with scale invariant feature transform or local binary pattern to provide a
comprehensive representation of pathological tissue. Integration of attention
gate and texture attention gate into skip connections of texture attention
UNet. Use of a 3D CNN as an ensemble of attention UNet and texture attention
UNet. Design of Generalized Dice loss, Weighted Pixel-wise Cross entropy loss,
and Boundary loss to address unbalanced data, and boundary between pancreas
and tumors Shen et al. (2022) Dilated pancreatic duct 82 (NIH) for
localization 30 (internal) for segmentation 3D UNet (Localization) 3D UNet +
Squeeze and excitation (Segmentation) Supervised Dice loss Focal loss NIH:
75.9% (DSC) 72.4% (Recall) Internal: 49.90% (DSC) 51.90% (Recall) First study
on automated 3D segmentation of dilated pancreatic duct. Generation of an
annotated dataset on dilated pancreatic duct. Attention block with squeeze and
excitation inserted into the bottleneck of a 3D UNet Chaitanya et al. (2021)
Tumors 282 (MSD) GAN + UNet Semi- supervised Adversarial loss 52.90% (DSC)
Semi-supervised learning for data augmentation. Adversarial term to help two
generators synthesize diverse set of shape and intensity variations present in
the population, even in scenarios where the number of labeled examples are
extremely low. Code available at: https://github.com/krishnabits001/
task_driven_data_augmentation

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main Contributions Huang et al. (2021b) Pancreatic neuroendocrine
neoplasms 98 (First Affiliated Hospital of Sun Yat-Sen University and Cancer
Center of Sun Yat-Sen University) 72 (from both above centers) UNet Supervised
Cross entropy loss First dataset: 81.80% (DSC) 83.60% (Precision) 81.40%
(Recall) Second dataset: 74.80% (DSC) 87.20% (Precision) 68.60% (Recall) A
radiologists identified tumors by drawing bounding boxes to delineate region
of interest sent as input to UNet. Radiomic analysis to predict
pathohistologic grading Si et al. (2021) Pancreatic ductal adenocarcinoma and
other types of tumors 319 for training (Second Affiliated Hospital Shanghai)
347 for testing (First and Second Affiliated Hospital Shanghai) ResNet18
(Localization) UNet32 (Segmentation) Supervised Cross entropy loss 83.70%
(DSC) Three different networks used for pancreas location, segmentation, and
diagnosis (presence of tumors) Wang et al. (2021c) Pancreatic ductal
adenocarcinoma 800 (John Hopkins) 281 (MSD) (generalization) UNet with
Inductive attention guidance Semi- supervised Cross entropy loss John Hopkins:
60.28% (DSC) 99.75% (Recall) MSD: 32.49% (DSC) Attention guided framework for
classification and segmentation with partially labeled data (few annotated
images for segmentation). Training using multiple instance learning with
cancer and background regions as bags instead of per-voxel pseudo labels as in
typical semi-supervised learning Turečková et al. (2020) Parenchyma and tumors
420 (MSD) + datasets of other organs UNet and VNet with Attention gate in skip
connections Supervised Dice loss Cross entropy loss Parenchyma (UNet): 81.81%
(DSC) 81.21% (Precision) 84.51% (Recall) Tumors (UNet): 52.68%(DSC) 62.98%
(Precision) 55.84% (Recall) Parenchyma (VNet): 81.22% (DSC) 80.61% (Precision)
84.10% (Recall) Tumors (VNet): 52.99%(DSC) 64.62% (Precision) 54.39% (Recall)
Attention gate integrated into skip connections for segmentation of pancreatic
tumors Xie et al. (2020) Parenchyma and pancreatic cysts 82 (NIH) 200 (John
Hopkins: renal donors) 131 (John Hopkins: pancreatic cysts) VGGNet with
Hierarchical recurrent saliency transformation network between Localization
and Segmentation Supervised Dice loss NIH: 84.53% (DSC) Renal donors: 87.74%
(DSC) Pancreatic cysts: 83.31% (DSC) Saliency transformation module beteween
first and second stage to transforms the segmentation probability map as
spatial weights, iteratively, from the previous to the current iteration.
Hierarchical version to segment first the pancreas and then the internal
cysts. Code available at: https://github.com/198808xc/ OrganSegRSTN

###  5.2 Tumors

The pancreatic ductal adenocarcinoma is a common malignant tumor of the
gastrointestinal tract and generally arises in the head of the pancreas (Du et
al., 2023). The accurate segmentation of pancreas tumors is essential for the
clinical integration with quantitative imaging biomarkers which have shown
promising results of early detection of pancreas tumors, and for precise 3D
modeling for surgical and radiotherapy planning (Mukherjee et al., 2023). The
reviewed studies on pancreas tumor segmentation are reported in Table 7.

####  5.2.1 Multi-stage methods

Si et al. (2021) used ResNet18 network for localization and UNet32 for
segmentation. The model was trained on a dataset with different types of
tumors from 319 patients from the Second Affiliated Hospital in Shanghai. It
was tested on an independent dataset of 347 patients from the First and Second
Affiliated Hospital in Shanghai. Mahmoudi et al. (2022) proposed a three-stage
method for the segmentation of pancreatic ductal adenocarcinoma and vessels.
They designed a texture attention UNet (TA-UNet) with texture attention block
with scale-invariant feature transform or local binary pattern block, and
attention gate block inserted into skip connections. The texture attention
block provided a comprehensive representation of the pathological tissue.
Firstly, 3D local binary patterns were employed to localize the pancreas.
Secondly, attention UNet and TA-UNet were used for segmentation. Finally, a
CNN aggregated attention UNet and TA-UNet (Mahmoudi et al., 2022). Shen et al.
(2022) reported the first work on segmentation of dilated pancreatic duct. A
3D UNet was first trained on the NIH dataset to localize the pancreas. A
second 3D UNet with a SE attention block, inserted into the bottleneck layer,
was trained on an internal dataset of 30 CTs for segmentation (Shen et al.,
2022). Ju et al. (2023) proposed a two-stage method for the segmentation of
parenchyma and tumors on NIH and MSD datasets, respectively. In the
localization stage, spatial-visual cue fusion and active localization offset
modules were added to UNet. A spatial visual cue fusion module, based on
conditional random field, learned global spatial context. It combined the
correlations between all pixels in the image to optimize the rough and
uncertain pixel prediction. The active localization offset module adjusted
dynamically the localization results during the coarse stage (Ju et al.,
2023). Zou et al. (2023) reported the first work on errors in segmenting
dilated pancreatic duct consisting of three stages. nnUNet was first used for
localization. Then, for the segmentation stage, they designed a terminal
anatomy attention module to learn the local intensity from the terminal part
of the dilated pancreatic duct in CT images, feature cues from the coarse
predictions, and global anatomy information. Finally, for the refine stage a
terminal distraction attention block was developed to reduce the false
positive and false negative cases. This method was initially trained and
evaluated on a dataset of 150 CTs from Nanjing Drum Tower with different types
of cancers, with PDAC as the most frequent. It was then assessed for
generalization on an external dataset of 40 CTs of pancreas ductal
adenocarcinoma from Jiangsu Province Hospital of Chinese Medicine. (Zou et
al., 2023).

####  5.2.2 Other methods

Turečková et al. (2020) integrated attention gate into skip connections of
UNet and VNet networks evaluated on MSD dataset for pancreas tumors in
addition to segmentation of liver and spleen. Huang et al. (2021b) proposed a
semi-automatic approach of segmentation of pancreas neuroendocrine tumors
where a radiologist localized the tumors by drawing bounding boxes to
delineate regions of interest, which were sent as input to UNet. The results
of segmentation were used for radiomics analysis. Two internal datasets of 98
and 72 CTs, respectively, were used for prediction of the grading of tumors
and prediction of recurrence (Huang et al., 2021b). Yang et al. (2022b)
designed AX-UNet integrating atrous spatial pyramid pooling into UNet, and a
loss function to address the blurry boundary issue. NIH and MSD datasets were
used for the segmentation of parenchyma and tumors, respectively (Yang et al.,
2022b). A similar approach was adopted by Ni et al. (2023) on an internal
dataset of 205 CTs to predict the recurrence of pancreas ductal adenocarcinoma
after surgery through radiomics on a distinct dataset of 64 patients. Li et
al. (2022b) conducted the first study on meta-learning from MRI to CT domain.
A CycleGAN was adopted for style transfer. It generated synthetic images with
continuously intermediate styles between MRI and CT to simulate domain shift.
Using a first meta-learning, the model learned the common knowledge of
synthetic data and provided knowledge for the cancer segmentation task. With a
second meta-learning, the model learned the salient knowledge of the CT data
to enhance segmentation. This method was trained and validated on 468 MRI and
163 CT from Jiangsu Province Hospital and Ruijin Hospital, respectively. Then
it was tested on 281 CTs of the MSD dataset. (Li et al., 2022b). Zhou et al.
(2023) proposed a dual branch encoder-decoder model (DB-Net) to first segment
the abnormal pancreas by combining semantic information and detailed
information extraction branches. The feature maps of the two branches were
then aggregated. A fine-grained enhancement encoder-decoder network (FE-Net)
was added to improve the segmentation accuracy of tumors. It consisted of a
contrast enhancement block after each encoding step to extract the edge detail
information, and a reverse attention block inverting the decoder feature to
guide the extraction of effective information in the decoder to generate an
accurate segmentation. An internal dataset of 116 abnormal and 42 normal
pancreases was used (Zhou et al., 2023). Wang et al. (2023) designed a 3D
UNet-like model with an encoder with a multi-modal fusion downsampling block
to fuse semantic information from PET to CT, and to preserve unique features
of different modal images. In the decoder, a multi-modal mutual calibration
block was designed to calibrate different scale semantics of one modal image
guided by attention maps from the other modal images. This method was trained
and tested on an internal dataset of 93 pancreatic cancer patients (Wang et
al., 2023). Qu et al. (2023) proposed transformer-guided progressive fusion
network (TGPFN), an encoder-decoder architecture with transformers to
complement the representation of CNN at multiple resolutions with global
representation. The encoder consisted of a Swin Transformer and 3D CNN
branches performing separate feature extraction. A progressive fusion between
CNN and transformer was performed in the decoder. A transformer guidance flow
module was designed to address the inconsistency of the feature resolution and
channel numbers between the CNN and transformer branches. A cross-attention
block was integrated into the CNN decoder to enhance the fusion capability
with the transformer. TGPFN was evaluated on three internal datasets and MSD
for the segmentation of parenchyma, tumors, and cysts. This model was trained
on an internal dataset of 313 patients from Peking Union Medical College
Hospital. It was then tested for generalization on 53 cases from Guangdong
General Hospital, and 50 from Jingling Hospital. All these three datasets
included several types of tumors. The model was also trained and evaluated on
420 cases of MSD dataset (Qu et al., 2023). Li et al. (2023e) designed a
nnUNet with attention and three modules based on temperature, i.e. balance
temperature loss to dynamically adjust weights between tumors and the
pancreas, a rigid temperature optimizer to avoid local optima, and a soft
temperature indicator to optimize the learning rate. He and Xu (2023) designed
an encoder-decoder Hybrid Transformer-CNN with Boundary-awareness (HTCB-Net)
network for the segmentation of parenchyma and tumors. The encoder consisted
of a Swin Transformer and an auxiliary boundary-extracting module to obtain
rich and discriminative feature representations. A boundary-preserving module
was inserted between the encoder and decoder to fuse boundary maps and
features from the encoder. For the decoder, a CNN was used (He and Xu, 2023).
Fang et al. (2023) proposed a UMRFormer, a UNet variant with two transformer
blocks embedded into the two lowest encoder-decoder steps. The transformers
coupled MSA with residual convolutional block to capture both local and global
features. Cao et al. (2023b) designed a multi-dimensional attention gate
combining three types of attention (spatial, channel, and multi-dimensional
features). The multi-dimensional attention gate was integrated into skip
connections for small target feature localization in multiple dimensions of
space and channels, and for filtering redundant information in shallow feature
maps, thus enhancing the feature representation of the pancreas and pancreatic
tumor. A loss function was designed to capture edge details of the pancreas
and tumors Cao et al. (2023b). Cao and Li (2024) proposed Strongly
Representative Semantic-guided Segmentation Network (SRSNet), a UNet-like
network with three modules, namely the high-resolution spatial information
recovery, the multi-scale high-resolution pre-segmented feature fusion, and
the pyramid multi-scale feature perception and fusion. In the first module,
the encoder and decoder features of the same layer were sent to a high-
resolution spatial information filtering block to extract high-resolution pre-
segmented images, which were then fused. In the second module, the features of
the encoder and decoder were finely processed into a high-resolution pre-
segmented feature map. This enabled the network not to rely on the feature
information in the last layer of the decoder. The third module used the
extracted pre-segmented images to guide the network to focus on the
dimensional changes of the segmented targets. The pre-segmented images
improved the network’s ability to segment lesion areas of different sizes
while reducing channel redundancy (Cao and Li, 2024). The methods proposed by
Cao et al. (2023b) and Cao and Li (2024) were trained on MSD for segmentation
of parenchyma and pancreas tumors and assessed for generalization only on
parenchyma using the NIH dataset.

###  5.3 Cysts

Pancreas cancer can originate from cystic lesions, which are fluid-filled sacs
and are increasingly common incidental findings on abdominal imaging tests
(Duh et al., 2023). Pancreatic cysts can be nonneoplastic and neoplastic. The
latter include benign lesions, such as serous cystadenomas, mucinous cystic
neoplasms (MCN), and intraductal papillary mucinous neoplasm, which may
degenerate into pancreas cancer (Duh et al., 2023). The reviewed studies on
cyst segmentation are listed in Table 7. Xie et al. (2020) proposed a two-
stage method for the segmentation of pancreatic cysts. A saliency
transformation module was inserted between the first and second stage to
transform the segmentation probability map as spatial weights, iteratively,
from the previous to the current iteration. A hierarchical version was
designed to segment first the pancreas and then the internal cysts on NIH, and
two internal datasets of 200 CTs (11 abdominal organs and five blood vessels)
and 131 biopsy-proven cases with pancreatic cysts (Xie et al., 2020). Li et
al. (2023g) designed UNet with a pyramid atrous attention module and spatial
pyramid pooling module inserted into the bottleneck layer to extract features
at different scales, and contextual spatial information, respectively. This
model was evaluated on an internal dataset of 107 CTs with pancreatic cysts.
Duh et al. (2023) proposed UNet with an attention gate to segment different
types of pancreatic cysts (serous cystadenomas, mucinous cystic neoplasms, and
intraductal papillary mucinous neoplasm) on an internal dataset of 335 CTs.

###  5.4 Inflammations

Acute pancreatitis, an inflammation of the pancreas, is the leading cause of
hospital admission for gastrointestinal disorders in the United States and
several other countries (Deng et al., 2023). The segmentation of an inflamed
pancreas is more challenging than the normal pancreas since it invades the
surrounding organs causing blurry boundaries, and it has higher shape, size,
and location variability than the normal pancreas (Deng et al., 2023). The
reviewed studies on segmentation of pancreas inflammation are listed in Table
8. Guo et al. (2022b) adopted UNet++ to segment chronic inflammation of the
common bile duct in pediatric patients. A ResUNet network was then used to
classify the degree of severity of inflammation. Deng et al. (2023) performed
the first study on the segmentation of acute pancreatitis on an internal
dataset of 89 CTs. An FCN with a region proposal was used for the detection of
pancreatitis region. The detected region was cropped and sent to the 2D U-Net
for segmentation (Deng et al., 2023).

Table 8:  Studies on pancreatitis

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main contributions Deng et al. (2023) Acute pancreatitis 89 (Internal)
FCN + Region proposal network (Detection) UNet (Segmentation) Supervised Focal
loss Cross entropy loss L1 regression loss 66.82% (DSC) FCN for detection of
pancreatitis region. The detected region was cropped and sent to the 2D U-Net
for segmentation. First study on segmentation on acute pancreatitis Guo et al.
(2022b) Chronic inflammation of choledoch 76 (internal) UNet++ Supervised
Binary cross entropy loss 83.90% (DSC) UNet++ to segment chronic inflammation
of cholecdoch in pediatric patients. Then ResUNet is used to classify the
degree of severity of inflammation

###  5.5 Semi-supervised learning

Wang et al. (2021c) designed an Inductive Attention Guidance Network (IAG-Net)
for classification and segmentation tasks, based on multiple-instance
learning. According to multiple-instance learning, a training set consists of
a group of bags, each containing several instances that are not labeled
individually. In contrast, the whole bag is assigned a label (Wang et al.,
2021c). In the approach proposed by Wang et al. (2021c), pseudo labels of
pancreas ductal adenocarcinoma and background regions were treated as bags
instead of per-voxel pseudo labels as in conventional semi-supervised
learning, addressing the problem of noise in per-voxel pseudo label. Each
labeled image was sent to a UNet. The resulting feature maps were used to
train an attention guidance module to learn the pancreas’s location. The bag
level pseudo labels were obtained by separating the pancreas location into two
regions (i.e. pancreas and background) based on a threshold level of attention
values. This model was trained on 800 CTs (400 with PDAC) from the John
Hopkins dataset and tested for generalization on MSD (Wang et al., 2021c).
Chaitanya et al. (2021) proposed a semi-supervised method for data
augmentation for tumors using two conditional GANs to model intensity and
shape variations present in populations and among CT scanners from different
centers. A generator of a conditional GAN received a labeled image and random
vector from a Gaussian distribution as input to generate a deformation field
which was applied to the original image and label. A second generator from
another conditional GAN with a labeled image and random vector from a Gaussian
distribution as input generated an additive intensity mask which was added to
the original input image. This approach was evaluated on the MSD dataset using
UNet for segmentation (Chaitanya et al., 2021).

###  5.6 Generalization to other datasets

Some studies assessed the generalization of the implemented models to external
datasets. These works were reported by Si et al. (2021); Wang et al. (2021c);
Li et al. (2022b); Cao et al. (2023b); Mukherjee et al. (2023); Qu et al.
(2023); Zou et al. (2023); Cao and Li (2024). The network architectures of
these studies were described in Sections 5.2, 5.3, and 5.4. Mukherjee et al.
(2023) initially trained and evaluated nnUNet on a dataset of 1,151 CTs of
PDAC from Mayo Clinic and MD Andersen Cancer Center. It was then evaluated for
generalization on 152 cases of MSD and 41 TCIA datasets. The models proposed
by (Cao et al., 2023b; Cao and Li, 2024) were trained on MSD for segmentation
of parenchyma and pancreas tumors and assessed for generalization only on
parenchyma using the NIH dataset.

###  5.7 Design of loss functions

Mahmoudi et al. (2022) proposed a combination of cross-entropy, Dice loss, and
boundary loss to address the low contrast between parenchyma and tumors. The
boundary loss was a differentiable surrogate of a metric ($BF_{1}$) more
sensitive to misalignments of boundaries than cross-entropy, Dice loss, and
IoU (Bokhovkin and Burnaev, 2019). Due to the difference of volumes of tumor
and parenchyma, soft Dice loss may lead to insufficient feature extraction of
tumors (Li et al., 2023e). Li et al. (2023e) introduced balance temperature
loss to dynamically adjust weights between tumors and parenchyma during
training to avoid ignoring feature extraction of the tumors. This was achieved
by inserting a parameter called Temperature which gradually decreased to shift
the network focus from the parenchyma to tumors. Temperature started from a
maximum value and was limited by a minimum value (Li et al., 2023e). Cao and
Li (2024) designed Difficulty-guided adaptive boundary-aware loss to address
the class imbalance issue and increase the network sensitivity to boundary
pixels. This loss used a category weight parameter to increase the
misclassification penalty for pixels in small target regions, making the
network focus on target regions. Adaptive boundary weights were added to
improve the segmentation of uncertain boundaries (Cao and Li, 2024).

###  5.8 Comparison of performances

MSD was the most adopted dataset (13 out of 25 reviewed works or 52.0%) since
it includes annotation of pancreas tumors (Section 3.7). Some studies employed
only the annotated 281 CTs of the MSD dataset, while others the whole 420
scans of MSD, including 139 unlabeled CTs. For this reason, the performances
of the reviewed studies on MSD varied greatly. The NIH dataset was employed in
four studies, in three of which where both parenchyma and tumors were
segmented, while in the fourth study, the NIH dataset was adopted by a two-
stage model for the localization task (Table 7). In contrast with the reviewed
studies on parenchyma, there was more use of private (internal and external)
datasets as alternative sources of data for the segmentation of cancer, cysts,
and inflammation (Table 7). As a consequence, a comparison among these studies
was not possible. UNet-based models reported the highest DSC score on the
portion of 281 annotated CTs of the MSD dataset. UNet coupled with channel
attention and multi-scale convolutions achieved 80.12% of DSC. The multi-scale
convolutions were embedded in each layer of the encoder to extract semantic
information at different scales to localize small or very small tumors, and
inserted also in the decoder layers (Du et al., 2023). This configuration
outperformed by a large margin a two-stage model, with UNet for both
localization and segmentation, reaching a DSC of 63.36% (Ju et al., 2023).
This model integrated a spatial visual cue fusion module, based on the
conditional random field to learn the global context, and an active
localization offset module to adjust dynamically the localization results
during the coarse stage (Ju et al., 2023). When considering all 420 CTs of the
MSD dataset, the highest DSC score (51.83%) was achieved by a hybrid
transformer. This model consisted of a Swin-transformer as an encoder with two
modules, the first as an auxiliary block for boundary extraction to obtain
rich and discriminative feature representation, and the second to preserve the
pancreas boundary. The decoder was a CNN (He and Xu, 2023). When used for
generalization on MSD, nnUNet reported a DSC of 82.00% on a portion of 152
cases of MSD after training on the dataset curated at Mayo Clinic of 921 CTs,
described by Panda et al. (2021) (Mukherjee et al., 2023). All the reviewed
studies on tumors were based on supervised learning, except one that concerned
semi-supervised learning for data augmentation using two generators of GANs.
UNet achieved a DSC of 52.90% on 282 cases of MSD (Chaitanya et al., 2021).
Overall, the DSC score for tumor segmentation on the MSD dataset dropped if
compared to the results for parenchyma on the same dataset, underlining the
further complexity due to the particularly small size of pancreas tumors (cfr.
Section 1, and Section 4.8). Segmentation of pancreatic cysts and inflammation
were investigated only with internal datasets (Table 7 and Table 8). UNet with
ASPP and spatial pyramid pooling (cfr. Section 5.3) reported a DSC of 84.53%
on a dataset of 107 cases of cysts, while UNet++ a DSC of 83.90% on 76 CTs for
pancreatitis (Li et al., 2023g).

##  6 Discussion

In this systematic review, we analyzed the published literature, consisting of
130 original studies, on DL for the segmentation of parenchyma, tumors, cysts,
and inflammation of the pancreas. By looking at the geographical origin of the
reviewed studies, China is leading the ranking with more than half of the
published articles in peer-reviewed journals, ahead of the United States, UK,
Japan, and Canada. Unexpectedly, there are countries with an established
tradition in pancreatic surgery, like Italy, not present in this ranking,
underlying a research gap from a technical point of view with the others (Abu
Hilal et al., 2023).

###  6.1 Clinical need perspective

Pancreas diseases like tumors are aggressive and in most cases, if not
promptly diagnosed, can become lethal. DL can streamline the 3D reconstruction
of radiological datasets, thus helping clinicians at the diagnosis stage,
provided that the quality of segmentation meets the clinical need.
Unfortunately, the pancreas has been traditionally regarded as one of the
toughest abdominal organs for the segmentation task due to its small volume
compared with a full CT scan, blurred boundaries, and large variations among
patients in terms of shape and position. DL methods are no exception, as
highlighted by the present systematic review. To fill this gap many methods of
DL segmentation have been proposed. The trend in the number of published
studies is constantly growing, thus reflecting an increase in interest in the
community. There are fewer studies on tumor segmentation as they more
challenging to segment than parenchyma. DL segmentation on other tiny
structures like the dilated pancreatic duct and surrounding vessels has been
only recently proposed, with initial studies published in 2022 (Mahmoudi et
al., 2022; Shen et al., 2022; Zou et al., 2023). Future developments should
consider the adaptability of the DL models to changes in the size and shapes
of the lesions over time. Models combining different modalities, e.g., CT and
MRI, should also be implemented to extract more information from the
radiological data. We observe that few studies of out the reviewed ones were
led by clinicians, with seven works published in journals in the medical field
(Bagheri et al., 2020; Guo et al., 2022b; Li et al., 2023g; Mukherjee et al.,
2023; Park et al., 2020; Si et al., 2021; Sundar et al., 2022). In contrast,
the rest of the studies were published in technical journals or cross-
disciplinary ones at the boundary between medicine and computer science.

###  6.2 DL models perspective

The accuracy of pancreas segmentation by DL models improved over the years, as
demonstrated by scores on DSC, and the Jaccard index on NIH dataset. However,
the score on segmentation of smaller lesions, like pancreas ductal
adenocarcinoma, on specific datasets for tumors like MSD is much lower.
Overall, the segmentation of a small organ like the pancreas presents a class
imbalance challenge with the background as the prominent class, followed by
parenchyma of the pancreas, and tumor as the least present. This issue was
mitigated by the adoption of the Dice loss function. As reported in Section
4.7 and Section 5.7, several studies proposed the design of new loss functions
to improve metrics results. Almost all the studies used region-based metrics
(e.g., DSC, and Jaccard index). Only 20 out of 105 on parenchyma, and 5 out of
26 on tumor and cysts segmentation used a boundary-based metric like HD. Two
works used NSD as a region-based metric (Ma et al., 2022; Tong et al., 2023).
Our review highlights an enormous variety of DL architectures specifically
designed for pancreas segmentation from standard UNet to transformers up to
hybrid transformers. Likewise, many attention blocks have been designed from
attention gate to SE up to reverse attention (Oktay et al., 2018; Zhou et al.,
2023). Although these models demonstrated improvements over the years on DSC,
Jaccard, and HD metrics, the results may show a limited value that is not the
marginal entity, as in most cases, of such improvement but the fact that the
models were trained and tested on datasets of small size and mostly from a
single center. By grouping the studies sharing the same application, DL
approach (e.g., multi-stage), and dataset we could compare the performances in
some cases. For instance, the present review highlighted that a UNet
configuration with residual blocks in the encoder and a decoder with spatial
and channel attention obtained the highest DSC score (91.37%) on the NIH
dataset, outperforming transformers-based models and other novel architectures
(Shan and Yan, 2021). This evidence is in agreement with a recent study
setting a comprehensive benchmark of current DL models for segmentation in
medical imaging (Isensee et al., 2024). However, since the data split for
training, validation, and test sets were different on the same dataset we
could not decree which models are the most suitable for pancreas parenchyma or
tumor segmentation. Furthermore, the small size of the test set introduces
result instability and questions the significance of the performance gains.
Other confounding factors precluding an objective comparison are the different
hardware capabilities (Isensee et al., 2024). Additionally, the
reproducibility of the results of most studies is challenging since the code
is publicly available only for a few of them.

###  6.3 Datasets perspective

By looking at the tables summarizing the studies (Appendix A, and Appendix B)
the first remark that stands out is that in the vast majority of studies, the
DL models are trained and tested only on publicly available datasets,
suggesting that there are difficulties in curating internal datasets. In
contrast, some institutions were capable of collecting large datasets, e.g.
with 1,917 CTs from Mayo Clinic and 1,150 CTs from John Hopkins Medical
Institution for the segmentation of parenchyma and pancreas ductal
adenocarcinoma, respectively (Panda et al., 2021; Wang et al., 2021c). Other
datasets, like AbdomenCT-1k, combined different datasets like NIH and MSD, and
extended them by labeling other organs (liver, spleen, and kidney) in addition
to the pancreas, in addition to combining data from different institutions,
from multiple vendors and acquired with different stages (arterial and venous)
(Ma et al., 2022). Currently, there is a need for new datasets on pancreas
tumors and vascular structures. One of the most frequent criticisms of AI
models is the lack of generalization to data from different institutions or
from different models of CT scanners by different vendors. Sections 4.6 and
Section 5.6 reported the results of some works on private (internal or
external) datasets for generalization. However, further work is required to
prove the robustness of DL models to external institutions. In addition to
being multicentric and multivendor, the new datasets for pancreas segmentation
(including tumors and vascular structures) should also compensate for bias in
the population selection, by including a more diverse range of ethnicities.
Semi-supervised and unsupervised learning look promising to exploit datasets
with few labeled data or with only unlabeled data, respectively. This review
analyzed eight and four works on semi-supervised and unsupervised learning for
parenchyma segmentation, respectively (Section 4.4, Section 4.5). For tumors
only one study used semi-supervised learning (5.5). According to the
literature, it seems that semi-supervised learning methods performed better
than supervised ones (Chen et al., 2022b). However, this is not the case for
the pancreas segmentation. For this task, supervised learning still provided
the higher scores, as documented by this systematic review.

###  6.4 Clinical translation perspective

What emerged from our analysis is a consistent push among research groups
towards designing more sophisticated DL models setting new benchmarks on
standard datasets. In contrast, there are also few studies using efficient and
high-performing DL models like nnUNet on more variegated datasets (multi-
center, multi-organ, and multi-vendor) as AbdomenCT-1k (Ma et al., 2022).
Overall, by considering the published literature, the following question
arises: "Should resources be directed towards refining existing models or
harnessing established networks with extensive, meticulously curated
datasets?" The answer still remains complex. From a clinical standpoint, the
tangible benefits for patients might be elusive if the DL models are not
rigorously tested in real-world clinical settings. According to the most
recent guidelines on minimally invasive pancreatic surgery, there exists no
corpus of evidence on the impact of AI in laparoscopic or robotic pancreatic
surgery. Most of the published studies assessed the technical feasibility of
utilizing AI. However, there is no demonstration of clinical implementation
and validation at multiple centers (Abu Hilal et al., 2023). Moreover,
transitioning from research to market poses formidable challenges, including
model generalizability, explainable AI, data privacy safeguards, regulations,
and certification. While federated learning seems a promising avenue for
training DL models across diverse institutions without compromising data
privacy, it is noteworthy that no published studies on this approach were
available for pancreas segmentation at the time of our systematic review.
Similarly, the application of explainable AI to pancreas segmentation remains
largely unexplored.

##  7 Conclusions

This systematic review of DL applications for segmenting the pancreas and its
lesions elucidates significant advancements and identifies important areas of
improvement. The review highlights several critical challenges, including the
limited availability of large datasets that represent the population well, and
the integration of these technologies into clinical settings, requiring real-
time segmentation capabilities that align with clinical workflows. This
integration is further complicated by the need for standardized evaluation
metrics and domain adaptation to ensure that models are generalizable across
different clinical environments and imaging modalities. Efforts to address
these challenges will improve the accuracy and efficiency of DL models and
enhance their applicability in real-world clinical scenarios, thereby bridging
the gap between technical capabilities and clinical needs.

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##  Appendix A Full list of the reviewed studies on the segmentation of
pancreas parenchyma

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main Contributions Dai et al. [2023] Segmentation of pancreas 82 (NIH)
281 (MSD) UNet (Localization) Deformable convolution Vision Transformer
(Segmentation) Supervised Binary cross entropy Dice loss NIH: 89.89% (DSC)
89.59% (Precision) 91.13% (Recall) MSD: 91.22% (DSC) 93.22% (Precision) 91.35%
(Recall) Skip connections integrating: vision transformer, deformable
convolutions, and scale interactive fusion (combining global and local
features, and merging feature maps of different scales). Two-dimensional
wavelet decomposition to solve the issue of blurred boundaries Fang et al.
[2023] Segmentation of pancreas 281 (MSD) 91 (Clinical Proteomic Tumor
Analysis Consortium Pancreatic Ductal Adenocarcinoma) U-shaped Encoder-Decoder
CNN + Transformer Supervised Dice loss Cross entropy loss MSD: 77.36% (DSC)
8.34 (95HD) Tumor dataset: 85.54% (DSC) 4.05 mm (HD95) A transformer module
with multi-head self-attention and residual convolutional block was designed
to capture both local and global features. Code available at:
https://github.com/ supersunshinefk/ UMRFormer-Net Francis et al. [2023]
Segmentation of liver, kidneys, spleen, and pancreas 1,112 (AbdomenCT-1k)
Conditional GAN: dilated UNet + Attention gate (Generator) Fully Convolutional
Network (Discriminator) Supervised Adversarial loss 86.10% (DSC) 6.65 mm
(HD95) 86.80% (Precision) 86.60% (Recall) Residual dilated convolution block
and spatial pyramid pooling replacing convolutions and max pooling in UNet.
Attention gate inserted into skip connections Ge et al. [2023] Segmentation of
pancreas 45: (Nanjing Drum Tower Hospital) (Reconstruction) + 15 (Nanjing
General PLA Hospital) for generalization + 90 (liver tumor) for generalization
Average Super Resolution GAN with: 3D CNN (Reconstruction) + 3D UNet for both
Localization and Segmentation Supervised Mean squared error loss Dice loss
Cross entropy loss Generalization (pancreas): 84.20% (DSC) 0.54 mm (ASD) GAN:
Super resolution network to reduce anisotropy resolution. A generator
reconstructs thin slices in z axis The discriminator optimizes the output of
generator. The optimized generated images are sent to a dual-stage network for
segmentation. Predictions on high-resolution are down-sampled to restore
initial resolution Huang et al. [2023] Segmentation of aorta, gallbladder,
spleen, kidneys, liver, pancreas, spleen, stomach, ventricles, myocardium, and
retina 30 (Synapse) 100 MRI (Automated cardiac diagnosis challenge) 40
(Digital Retinal Images for Vessel Extraction) Encoder-decoder with:
transformer blocks in all encoding and decoding steps + Transformer context
bridge between encoder and decoder (fusion of multi-scale information)
Supervised – 65.67% (DSC) Hierarchical encoder-decoder with ReMix-FFN module
in each transformer block with a convolution and a skip connection between the
two fully connected layers to capture local information in addition to global
dependencies. Features of different scale as output of each encoder step are
concatenated, and sent to ReMixed transformer context bridge with self-
attention to capture global dependencies. The output features are split into
different scale feature maps and sent to ReMix-FFN modules of the decoder to
mix global dependencies with local context. Code available at:
https://github.com/ZhifangDeng/ MISSFormer

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main Contributions Irshad et al. [2023] Segmentation of pancreas 82
(NIH) 47 (BTCV) UNet UNet++ Attention-UNet Two topologies: sharing encoder and
decoder, but task-specific final layers; or sharing endoder with task-specific
decoders Supervised Dice loss Binary cross entropy NIH: 82.00% (DSC) 0.673 mm
(HD) 85.90% (Precision) 82.20% (Recall) BTCV: 77.80% (DSC) 0.919 mm (HD)
81.00% (Precision) 77.00% (Recall) Multi-task network for: region segmentation
and edge prediction. Comparison of different network topologies. Code
available at: https://github.com/samra-irshad/ 3d-boundary-constrained-
networks Jain et al. [2023] Segmentation of pancreas 82 (NIH) K-mean and
Gaussian mixture model (Unsupervised) (Localization) UNet, Holistically-Nested
Edge Detection, and Dense-Res-InceptionNet (Segmentation) Unsupervised +
Supervised Dice loss 81.75% (DSC) 83.03% (Precision) 81.70% (Recall)
Unsupervised localization of pancreas after segmenting liver and spleen using
K-means and Gaussian mixture models Li et al. [2023c] Segmentation of pancreas
82 (NIH) 281 (MSD) 104 (Private) UNet with: Meta-learning (Localization)
Latent-space feature flow generation (Segmentation) Supervised Design of
adaptive loss with: Recall loss, Cross entropy and Dice loss NIH (trained on
MSD and private): 80.24% (DSC) 1.92mm (ASD) MSD (trained on NIH and private):
81.09% (DSC) 1.99 mm (ASD) Private (trained on NIH and MSD): 84.77% (DSC) 1.28
mm (ASD) First generalization model for pancreas segmentation. Model-agnostic
meta-learning to improve generalization of the coarse stage. Appearance-style
feature flow generation in the fine stage to generate a sequence of
intermediate representations between different latent spaces for simulating
large variations of appearance-style features Li et al. [2023d] Segmentation
of pancreas 82 (NIH) 281 (MSD) 104 (Renji Hospital Shanghai Private dataset)
UNet with meta-learning (Localization) 3D UNet: Global feature contrastive
learning 3D UNet: Local image restoration (Segmentation) Self- supervised
Binary cross entropy loss Dice loss Squared error loss Adversarial loss
Training on NIH Generalization on MSD: 66.73% (DSC) Generalization on Private:
73.85% (DSC) Training on MSD Generalization on NIH: 76.71% (DSC)
Generalization on Private: 83.50% (DSC) Training on Private Generalization on
NIH: 65.03% (DSC) Generalization on MSD: 70.08% (DSC) Dual self-supervised
generalization model to enhance characterization of high-uncertain regions.
Global-feature self-supervised contrastive learning reducing the influence of
extra-pancreatic tissues. Local image restoration self-supervised module to
exploit anatomical context to enhance characterization of high-uncertain
regions Li et al. [2023f] Segmentation of liver, kidney, spleen, and pancreas
500 from AbdomenCT-1k 240 from AMOS-CT 3D UNet (Localization) UNet with:
Multi-branches feature attention (Encoder) + Feature attention aggregation
(Decoder) (Segmentation) Supervised Dice loss First dataset: 86.20% (DSC)
Second dataset: 78.40% (DSC) Network with self-adjustable attention and
receptive field size to segment liver, kidney, spleen, and pancreas. Different
kernel sizes to capture different scale features of different organs using:
multi-branch feature attention with four branches, and feature attention
aggregation with two branches Liu and Zheng [2023] Segmentation of liver,
spleen, pancreas, and kidneys 30 (BTCV) 281 (MSD) + partially labeled datasets
of other abdominal organs nnUNet Semi- supervised Cross entropy loss Dice loss
(labeled data) Context-aware voxel-wise contrastive learning loss (unlabeled
data) 83.60% (DSC) 4.30 mm (HD95) Exploiting unlabeled information in
partially labeled datasets. Context-aware voxel-wise contrastive learning
inserted into the bottleneck layer of a 3D nnUNet to increase context
awareness in patch-based strategy Paithane [2023] Segmentation of pancreas 80
(NIH) ResNet18 VGG-16 VGG-19 Supervised Cross entropy Dice loss 88.68% (DSC)
98.82% (Jaccard) 68.22% (Precision) 98.66% (Recall) Data augmentation to
reduce class imbalance Qiu et al. [2023] Segmentation of pancreas 82 (NIH)
DeepUNet (Localization) Residual transformer UNet (Segmentation) Supervised
Dice loss with Hausdorff distance term 86.25% (DSC) UNet like network with
each convolutional block consisting of residuals blocks, residual
transformers, and dual convolution down-sampling (for translational
equivariance)

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main Contributions Shen et al. [2023] Segmentation pancreas, duodenum,
gallbladder, liver, and stomach 42 (NIH) UNet with: Spatial attention
(location and size of organs) + Dilated convolution + Multi-scale attention
Supervised Dice loss Cross entropy loss 75.42% (DSC) 61.84% (Jaccard) 19.99 mm
(HD) Spatial attention to highlight location and sizes of target organs
(pancreas, duodenum, gallbladder, liver, and stomach). Deformable
convolutional blocks to deal with variations in shapes and sizes. Skip
connections with multi-scale attention to eliminate interference of complex
background Tian et al. [2023] Segmentation of pancreas 200 (from AbdomenCT-1k)
82 (NIH) 281 (MSD) 50 (Jiangsu Province Hospital) (Generalization) nnUNet
(Localization) + Hybrid variational model to capture weak boundaries
(Segmentation) Supervised Cross entropy Dice loss AbdomenCT-1k: 89.61% (DSC)
NIH: 87.67% (DSC) MSD: 87.13% (DSC) Generalization: 90.72% (DSC) First stage:
3D CNN for coarse segmentation Second stage: a new hybrid variational model to
capture the pancreas weak boundary Tong et al. [2023] Segmentation of liver,
kidney, spleen, and pancreas 511: 80 (NIH) 281 (MSD) + datasets of other
organs (multi-center) Encoder-Decoder (Localization) ResUNet and Multi-scale
Attention (Segmentation) Supervised Dice loss (Localization) Dice loss Mean
square error (Segmentation) 59.10% (DSC) 42.20% (NSD) Coarse stage for
localization. Fine stage with multi-scale attention to segment pancreas,
liver, spleen, and kidney. Wu et al. [2023] Segmentation of pancreas, left
ventricle, myocardium, right ventricle, and colon 80 (NIH) + datasets of other
organs V-Net + Attention Semi- supervised Cross entropy Dice loss 74.03% (DSC)
59.70% (Jaccard) 2.12 voxel (ASD) 9.10 voxel (HD95) Instead of using model
predictions as pseudo labels, high-quality pseudo labels are generated by
comparing multiple confidence maps produced by different networks to select
the most confident one (a compete-to-win strategy. A boundary-aware
enhancement module was integrated to enhance boundary discriminative features.
Code available at: https://github.com/ Huiimin5/comwin Xia et al. [2023]
Segmentation of pancreas 82 (NIH) VNet + Multi-dimensional Feature attention
Semi- supervised Cross entropy Dice loss Mean square error 79.55% (DSC) 66.87%
(Jaccard) 7.67 mm (HD95) 1.65 mm (MSD) Multi-dimensional feature attention and
improved cross pseudo supervision to effectively use unlabeled data reducing
the need of labeled data Yuan et al. [2023] Segmentation of aorta,
gallbladder, kidneys, liver, pancreas, spleen, and pancreas 30 (Synapse) UNet-
like with: Gated recurrent units for skip connections + Gated-dual attention
(Multi-scale weighted channel attention + Transformer self attention)
Supervised – 62.77% (DSC) Gate recurrent units integrated into skip
connections to reduce the semantic gap between low and high-level features.
Gated-dual attention to capture information on small organs and global
context. Code available at: https://github.com/DAgalaxy/ MGB-Net Zheng and Luo
[2023] Segmentation of pancreas 80 (NIH) Encoder-decoder for both Localization
and Segmentation Supervised Weighted binary cross entropy loss 85.58% (DSC)
74.99% (Jaccard) 86.59% (Precision) 85.11% (Recall) Extension-contraction
transformation network with a shared encoder for feature extraction and two
decoders for the prediction of the segmentation masks and the inter-slice
extension and contraction transformation masks Zhu et al. [2023] Segmentation
of pancreas 82 (NIH) 70 (Zheyi): Zhejiang University Hospital Adversarial
network + 3D ResUNet + Attention (Squeeze-Excitation) Supervised and
Unsupervised Dice loss Cross entropy loss NIH (supervised): 85.45% (DSC) Zheyi
(unsupervised): 75.43% (DSC) Training with 3D ResUNet and attention module
using pairs of labeled images fro one center and unlabeled ones from a
different center to generate multi-scale feature maps. Labeled and unlabeled
data are then trained by a discriminator for domain identification

Author Application Dataset Size Model Architecture Learning Strategy Loss
Results Main Contributions Chen and Wan [2022] Segmentation of pancreas 82
(NIH) UNet with Transformers in skip connections Supervised Dice loss Focal
loss 86.80% (DSC) 76.90% (Jaccard) 87.60% (Precision) 88.00% (Recall) 3D
channel transformer in the skip connections of UNet. Attention module in each
encoder level with project and excite block to enhance extraction of context
information. Cross attention between output of each transformer and decoder to
eliminate semantic inconsistency Chen et al. [2022d] Segmentation of pancreas
82 (NIH) 281 (MSD) Encoder-Decoder Attention feature fusion (Localization)
Encoder-Decoder Attention feature fusion Coordinate Multi-scale Attention
(Segmentation) Supervised Dice loss Binary cross entropy NIH: 85.41% (DSC)
74.80% (Jaccard) 85.60% (Precision) 85.90% (Recall) MSD: 70.00-80.00% (DSC)
60.00% (Jaccard) 80.00-90.00% (Precision) 60.00-70.00% (Recall) Attention
feature fusion on low and high level features to keep context. Multi-scale
attention to aggregate long-range dependencies, positional information, and
exploit multi-scale spatial information Chen et al. [2022c] Segmentation of
pancreas 82 (NIH) 281 (MSD) VGG-16 with Attention gate (Localization) VGG-16
with Residual multi-scale dilated attention (Segmentation) Supervised Dice
loss Binary cross entropy NIH: 85.19% (DSC) 74.19% (Jaccard) 86.09%
(Precision) 84.58% (Recall) MSD (generalization): 76.60% (DSC) 62.60%
(Jaccard) 87.70% (Precision) 69.20% (Recall) Attention gate used in the
localization stage to suppress irrelevant background regions. Weight
conversion module to transform segmentation map of the first stage into
spatial weights to refine input of the second stage. Residual multi-scale
dilated attention to exploit inter-channel relationships and extract multi-
scale spatial information. Code available at:
https://github.com/meiguiyulu/TVM Chen et al. [2022b] Segmentation of pancreas
82 (NIH) 281 (MSD) UNet (Localization) Unet with: Fuzzy skip connection +
Target attention in the decoder (Segmentation) Supervised Dice loss NIH: