# Brain Stroke Segmentation Using Deep Learning Models: A Comparative Study

Ahmed Solimana, Yousif Yousifa, Ahmed Ibrahima, Yalda Zafari-Ghadima, Essam A.
Rashedb and Mohamed Mabroka a Mathematics Program, Department of Mathematics,
Statistics and Physics, College of Arts and Sciences, Qatar University,
P.O.Box 2713, Doha, Qatar.bGraduate School of Information Science, University
of Hyogo, Kobe 650-0047, Japan

###### Abstract

Stroke segmentation plays a crucial role in the diagnosis and treatment of
stroke patients by providing spatial information about affected brain regions
and the extent of damage. Segmenting stroke lesions accurately is a
challenging task, given that conventional manual techniques are time-consuming
and prone to errors. Recently, advanced deep models have been introduced for
general medical image segmentation, demonstrating promising results that
surpass many state-of-the-art networks when evaluated on specific datasets.
With the advent of the vision Transformers, several models have been
introduced based on them, while others have aimed to design better modules
based on traditional convolutional layers to extract long-range dependencies
like Transformers. The question of whether such high-level designs are
necessary for all segmentation cases to achieve the best results remains
unanswered. In this study, we selected four types of deep models that were
recently proposed and evaluated their performance for stroke segmentation: (1)
a pure Transformer-based architecture (DAE-Former), (2) two advanced CNN-based
models (LKA and DLKA) with attention mechanisms in their design, (3) an
advanced hybrid model that incorporates CNNs with Transformers (FCT), and (4)
the well-known self-adaptive nnU-Net framework with its configuration based on
given data. We examined their performance on two publicly available datasets,
ISLES 2022 and ATLAS v2.0, and found that the nnU-Net achieved the best
results with the simplest design among all. Additionally, we investigated the
impact of an imbalanced distribution of the number of unconnected components
in each slice, as a representation of common variabilities in stroke
segmentation. Revealing the robustness issue of Transformers to such
variabilities serves as a potential reason for their weaker performance.
Furthermore, nnU-Net’s success underscores the significant impact of
preprocessing and postprocessing techniques in enhancing segmentation results,
surpassing the focus solely on architectural designs.

###### Index Terms:

stroke segmentation, vision Transformer, convolutional neural network, nnU-
Net, deep learning

##  I Introduction

Stroke, the second leading cause of morbidity and mortality worldwide, occurs
due to sudden disruptions in cerebral blood flow that result in neurocellular
damage or death [1, 2]. Medical imaging modalities such as magnetic resonance
imaging (MRI) and computed tomography (CT) offer valuable information on
stroke location, time, and severity [3, 4, 5]. Stroke segmentation plays a
crucial role by providing spatial information about affected brain regions and
the extent of damage, aiding in diagnosis and treatment. However, accurate
segmentation of stroke lesions remains challenging, as traditional manual
methods are prone to errors and time consuming.

Deep neural networks have achieved state-of-the-art results in numerous
computer vision tasks, including medical image segmentation, by learning
intrinsic patterns in a data-driven manner [6]. Convolutional neural networks
(CNNs) have been extensively utilized in segmentation architectures like U-Net
[7]. However, Conv-based models generally have limitations in modeling long-
range dependencies and providing global information due to their limited
receptive field [8], which is essential for tasks that require a global
understanding of the entire input. Vision Transformers [9, 10], inspired by
the Transformer model introduced in natural language processing [11], can
model long-range dependencies in data, allowing the network to capture complex
spatial relationships. The core idea in Transformers is the self-attention
mechanism [11], which enables models to weigh different parts of the input
differently and consider the most relevant parts. In general, vision
Transformers lack some useful inductive biases that Conv-based models have,
such as translation equivariance and locality. To address this issue and
capture more comprehensive information from the data, hybrid Conv-Transformer
models have been widely used [12, 13, 14, 15].

Most previous research on stroke segmentation using deep models is based on
Conv-based U-shaped architectures [16, 17, 18], but in recent years some
hybrid Conv-Transformer models have also been introduced [19, 20]. [21]
incorporated a fusion of 2D and 3D convolutions to facilitate an effective
representation of infarcts to enhance the segmentation results. Some research
studies employed additional blocks or advanced versions of convolutions to
increase the receptive field, to extract local and global information. For
instance, [22] employed the pyramid pooling module [23], where various kernel
sizes were used to aggregate multi-scale features from the data. Another study
by [24] also employed multi-kernels of various sizes to extract feature
information across different receptive fields. Pool-UNet, introduced by [25],
employed Squeeze and Excitation blocks [26] in the bottleneck to provide more
informative features for the decoder path by capturing interdependencies
between channels. Additionally, a combination of CNNs and the Poolformer
structure [27] was utilized to benefit from both local and global information.
[28] employed Large Kernel Attention [29] to take advantage of the inherent
biases of CNNs while capturing long-range dependencies.

Some studies have introduced hybrid models to leverage the strengths of CNNs
and Transformers for more accurate stroke segmentation. In UCATR [30], an
architecture was proposed consisting of a CNN-based encoder and decoder, with
a Transformer module facilitating the extraction of global information. By
using multi-head cross-attention modules in the skip connections, irrelevant
information was filtered in this model. In another work, a model named
UTransNet [31] was introduced, where the main blocks of the encoder and
decoder paths consisted of two convolutional layers followed by a Transformer
module. In the METrans model [32], multiple Convolutional Block Attention
Modules (CBAM) [33] were used to take advantage of channel and spatial
attention. Incorporating a Transformer block in the bottleneck of the U-shaped
architecture proved useful for extracting global information. A dual-path
model was also proposed, known as LLRHNet [34], which consisted of a CNN
encoder and a Transformer-based encoder; the extracted features from these
encoders were then fused. Some studies also employed hybrid models to mitigate
the issue of oversmooth boundary estimation for fuzzy stroke infarcts in their
proposed models [35, 36].

Although variabilities between patients, vendors, imaging artifacts, and noise
present challenges for deep networks in most medical image segmentation tasks,
this issue is more severe for stroke segmentation. Unlike organ segmentation
tasks, such as cardiac segmentation, where certain characteristics like shape
and location may be common across cases, stroke segmentation lacks such
universally applicable features. Variations in stroke infarct size, number,
location, shape, pattern, and difference in intensity based on their age pose
significant difficulties for deep models. These unresolved challenges make the
current proposed pipelines less applicable to accurately segmenting the stroke
[37].

Recently, advanced deep models have been introduced for general medical image
segmentation, showcasing promising results that surpass many state-of-the-art
networks when evaluated on specific datasets. However, these models have not
been evaluated for stroke segmentation. In this paper, we selected four
architectures that were recently proposed and evaluated their performance for
stroke segmentation using two publicly available datasets. To gain a better
understanding of models based on their design by CNNs or Transformers for
stroke segmentation, we included a pure Transformer-based model (DAE-Former),
two CNN-based models (LKA and DLKA), an advanced model that incorporates CNNs
within Transformers (FCT), and the self-adaptive framework of nnU-Net.

The paper’s contribution can be summarized as follows:

  * •

The paper evaluates the performance of four different types of recently
developed deep models for medical image segmentation for stroke segmentation.

  * •

The evaluation is conducted on two large datasets with different modalities,
ISLES 2022 and ATLAS v2.0, providing a comprehensive comparison across
different architectures.

  * •

The study identifies that the nnU-Net framework achieves the best results
among all evaluated models for stroke segmentation, despite its simpler design
compared to Transformer-based architectures and other advanced CNN-based
models.

  * •

The paper sheds light on the importance of convolutional layers within stroke
segmentation architectures, highlighting their ability to capture local
information, which is crucial for accurate segmentation. Also, the paper
underscores the significant impact of preprocessing and post-processing
techniques in enhancing segmentation results, suggesting that focusing solely
on architectural designs may not be sufficient for achieving optimal
performance in medical image segmentation tasks like stroke segmentation.

The paper is organized as follows: Section 2 provides an overview of the deep
architectures employed in this study for stroke segmentation, along with the
utilized datasets. Section 3 provides information on the evaluation metrics
used to assess the performance of different architectures, implementation
details, and the training process. It also summarizes our findings and
includes a comparison of the performance of different models on two public
datasets with a discussion related to various aspects of stroke segmentation
using recently introduced deep architectures. Finally, Section 4 presents our
conclusions based on the results of this research effort and highlights the
limitations of our experiments.

##  II Architectures and Datasets

In this section, we briefly survey four different types of recently developed
deep models for medical image segmentation, which will be used in our
evaluation for stroke segmentation. The architectures were selected based on
their architectural designs and superior performance compared to other
networks. Table I summarizes their performance for different benchmark
datasets including Synapse111https://doi.org/10.7303/syn3193805, ACDC [38],
and ISIC 2018 [39], and the state-of-the-art methods they surpassed, as
mentioned in their original papers.

TABLE I: Comparison of performance across different architectures on different datasets, including the baseline networks they have surpassed in terms of performance as reported in the original paper. Architectures | Synapse | ACDC | ISIC 2018 |  Surpassed Networks
---|---|---|---|---
DAE-Former [40] | 0.8263 | - | 0.9147 |  U-Net [7], Att-UNet [41], TransUNet [8], Swin-UNet [42], LeViT-UNet [43], MT-UNet [44], TransDeepLab [45], HiFormer [46], MISSFormer [47], MCGU-Net [48], MedT [49], FAT-Net [50], TMU-Net [51], TransNorm [52]
FCT [53] | 0.8353 | 0.9302 | - |  U-Net [7], Att-UNet [41], TransUNet [8], TransClaw U-Net [54], Swin-UNet [42], LeViT-UNet [43], MT-UNet [44], nnFormer [14]
LKA [55] | 0.8277 | - | 0.9118 |  TransUNet [8], Swin-UNet [42], LeViT-UNet [43], MISSFormer [47], Hi-Former [46], DAE-Former [40], TransDeepLab [45], PVT-CASCADE [56], DAGAN [57], MCGU-Net [48], MedT [49], FAT-Net [50], TMU-Net [51], DeepLabv3+ [58]
D-LKA [55] | 0.84277 | - | 0.9177 |  TransUNet [8], Swin-UNet [42], LeViT-UNet [43], MISSFormer [47], ScaleFormer [59], Hi-Former [46], DAE-Former [40], TransDeepLab [45], PVT-CASCADE [56], DAGAN [57], MCGU-Net [48], MedT [49], FAT-Net [50], TMU-Net [51], DeepLabv3+ [58]
nnU-Net [60] | 0.8880 [61] | 0.9161 [53] | - |  -

###  II-A Architectures

####  II-A1 nnU-Net

The No new U-Net (nnU-Net) framework [60] stands out as an exceptional tool,
building on the well-established U-Net architecture and incorporating
innovative self-adaptive capabilities. In contrast to traditional methods that
rely on manual tuning, nnU-Net autonomously adjusts itself for new tasks,
simplifying key aspects from preprocessing to post-processing with minimal
human involvement. What distinguishes nnU-Net is its straightforwardness and
emphasis on fundamental elements, resulting in remarkable performance across
various challenges. It has demonstrated its effectiveness by surpassing
existing methods across 23 public datasets, highlighting its adaptability and
resilience in diverse segmentation tasks, including stroke segmentation [62,
63]. Departing from the original U-Net architecture, nnU-Net incorporates
leaky ReLUs and instance normalization, underscoring the potential efficacy of
non-architectural adjustments over architectural modifications. See Figure 1
for an illustration of nnU-Net’s pipeline for automated configuration when
provided with a new dataset.

Figure 1: nnU-Net’s pipeline for designing a segmentation network for each
dataset [60].

####  II-A2 DAE-Former

Dual Attention-guided Efficient Transformer (DAE-Former) [40] is a pure
Transformer-based U-shape architecture designed for medical image
segmentation. It has demonstrated superior performance over many state-of-the-
art architectures for multi-organ cardiac and skin lesion segmentation tasks.
This architecture incorporates three types of attention mechanisms: (1)
efficient attention [64], (2) transpose attention [65], and (3) cross
attention. See Figure 2 for an illustration of the DAE-Former structure.

By incorporating efficient attention and transpose attention, dual-attention
blocks were developed and utilized in both the encoder and decoder paths.
These blocks aim to capture both spatial and channel information, thereby
enhancing the model’s capacity to capture more informative feature
information. The dual attention blocks start with patch embedding modules,
extracting overlapping patch tokens from the input image. Efficient attention
is a modified version of the self-attention mechanism, the core element of
Transformers. It addresses the quadratic computational complexity issue,
making it more applicable for use with high-resolution medical images. In
regular Transformers, after producing the query (Q), key (K), and value (V)
vectors from the input sequence by mapping it onto the embedding dimension
(d), self-attention is calculated as follows:

$SA(Q,K,V)=Softmax(\frac{Q{\times}K^{T}}{\sqrt{D}}){\times}V$ (1)

Efficient attention utilizes normalization functions ($\rho$) for the query
and key, employing softmax normalization functions, and then calculates the
multiplications to produce the final representation. The process can be
formulated as:

$EA(Q,K,V)={\rho}_{q}(Q){\times}({\rho_{k}(K)}^{T}{\times}V)$ (2)

For capturing channel-related information, cross-covariance attention, also
known as transpose attention, is employed. This transpose attention is
calculated as follows:

$TA(Q,K,V)=V{\times}Softmax(\frac{K^{T}{\times}Q}{\tau})$ (3)

in which $\tau$ is employed to restrict the magnitude of the query and key
matrices through L2-normalization, improving stability during training.
Another crucial attention module is skip-connection cross-attention (SCCA).
Skip connections, commonly utilized in U-shaped architectures, help preserve
the flow of information across different scales. While plain skip connections
typically involve concatenating features extracted from encoder layers to
decoder layers, advanced versions such as attention-based connections can be
employed to suppress irrelevant regions in the image during training and
highlight significant features for the segmentation task. In DAE-Former, the
efficient attention structure is utilized for SCCA, with the distinction that
the input used for the query originates from the encoder layers, while the
inputs for keys and values are derived from the decoder layers.

Figure 2: Structure of the DAE-Former architecture [40]. The central component
of this architecture is the Dual Transformer, comprising two distinct
attention mechanisms: efficient attention for capturing spatial information
and transpose attention for capturing channel information. Additionally, the
skip connection cross-attention (SCCA) module is employed to integrate
information from encoder layers with features from decoder layers. This fusion
process enhances extracted features by preserving the most relevant
information.

####  II-A3 FCT

This architecture, introduced as the first fully convolutional Transformer
(FCT) [53], is designed to be a robust and accurate network for medical image
segmentation. It incorporates CNN layers into the architecture of Transformers
to leverage CNNs’ ability to represent image features and Transformers’
capability to model long-range dependencies. In FCT, all projection layers in
the Transformer architecture are replaced by depth-wise convolutional layers,
providing an option to reduce computational costs and obtain a better spatial
context from images.

The resulting overlapping patches are fed into the Transformer encoder block
to apply the self-attention mechanism. Each FCT layer starts with a sequence
of operations: Layer Normalization, Convolution, Convolution, and MaxPool.
These convolutional layers are followed by a GELU activation function. The
output of the Transformer encoder block is then processed through the Wide-
Focus module, which is a multi-branch convolutional paradigm adapted to
enhance spatial context. One layer applies spatial convolution, while the
others apply dilated convolutions with increasing receptive fields. Extracted
features from multiple branches are fused via summation and passed into a
spatial convolution operator as a feature aggregation layer. See Figure 3 for
an illustration of the main blocks utilized in FCT architecture.

Additionally, the architecture adopts a multi-scale input for its U-shaped
design by concatenating resampled versions of the input to the encoder
features at multiple scales. This approach enhances the model’s ability to
capture features at different resolutions, contributing to improved
segmentation performance.

Figure 3: Illustration of the main blocks in FCT [53]. Each block comprises
two key modules: Convolutional Attention and Wide Focus. Following initial
convolutional layers, data undergoes processing through Convolutional
Attention, akin to conventional vision Transformers. However, instead of MLP
layers serving as projection layers, three layers of depth-wise convolutions
are employed. Subsequently, the output is fed into the Wide Focus module,
which consists of three convolutional layers: one standard and two dilated
layers with varying kernel sizes to augment the receptive field. The outputs
of these layers are summed and passed through an additional convolutional
layer for further processing.

####  II-A4 D-LKA

LKA (Large Kernel Attention) [29] employs convolutional kernels significantly
larger than those in traditional methods, capturing a broader contextual view
akin to the receptive fields observed in self-attention mechanisms.
Remarkably, LKA achieves this expanded field of view with substantially fewer
parameters compared to self-attention, leading to improved computational
efficiency utilizing a strategic combination of depth-wise and dilated
convolutions within these large kernels. The utility of LKA in medical image
segmentation, particularly in improving the representation of complex
structures such as lesions or organ deformations, is underscored by its
integration with Deformable Convolutions [66], further refining the method’s
adaptability and accuracy.

Unlike LKA, which uses fixed kernel sizes and shapes, Deformable Large Kernel
Attention (DLKA) [55] introduces the ability to dynamically adjust the
sampling grid. This adjustment is achieved by applying whole-numbered offsets
for free deformation, learned from the feature maps themselves. Refer to
Figure 4 for a visualization of the main blocks employed in DLKA and LKA
architecture, while Figure 5 demonstrates the functionality of deformable
convolutions. Consequently, DLKA facilitates an adaptive convolution kernel
capable of flexibly adjusting its shape to better represent complex anatomical
structures. DLKA employs an additional convolutional layer dedicated to
learning these deformations and creating an offset field. This layer aligns
with the kernel size and dilation rate of its corresponding convolutional
layer in the DLKA module. For this study, we evaluated both D-LKA and LKA
architectures to assess the impact of deformable-based convolutions compared
to regular ones.

Figure 4: Illustration of the main blocks in D-LKA [55]. Following processing
by a convolutional layer, the data is inputted into the large kernel attention
block. This block first applies a sequence of operations: a deformable depth-
wise convolutional layer, followed by a deformable depth-wise dilated
convolutional layer, and finally a regular convolutional layer. The attention
mechanism, implemented through multiplication, serves to suppress irrelevant
information, facilitating effective learning during training. Notably, in the
LKA architecture, all convolutions are non-deformable, while maintaining the
same underlying structure.  Figure 5: Illustration depicting the concept of
deformable convolutions, a technique that enhances standard grid sampling
positions used in regular convolutions by integrating 2D offsets. This
modification enables the sampling grid to flexibly deform, with the offsets
learned from preceding feature maps through additional convolutional layers.
As a result, this approach conditions deformation based on input features,
offering localized, dense, and adaptable adjustments.

###  II-B Datasets

Among the datasets available for stroke segmentation, we selected two based on
their accessibility and the number of data they offer. This decision ensures a
sufficiently large dataset for training deep neural networks effectively.

####  II-B1 ISLES 2022

The Ischemic Stroke Lesion Segmentation (ISLES) dataset serves as an important
resource in the field of stroke lesion segmentation. The 2022 version of ISLES
comprises 400 MRI cases sourced from multiple vendors, with 250 publicly
accessible cases and 150 private ones [67]. This dataset offers a
comprehensive view of ischemic stroke lesions, showcasing diverse infarct
patterns, variable lesion sizes, and locations. Furthermore, the heterogeneity
of the data set, resulting from the use of imaging devices from three
different medical centers, presents a valuable opportunity to assess the
generalization of the proposed methods. Modalities encompass DWI, ADC, and
FLAIR images, making it a valuable resource for advancing research on stroke
lesion segmentation. See Figure 7 for some samples from this dataset.

####  II-B2 ATLAS v2.0

The Anatomical Tracings of Lesions After Stroke (ATLAS) datasets [68] are
available in two versions: 1.2 and 2.0, both featuring high-resolution
T1-weighted MRI images accompanied by the corresponding lesion masks. Version
1 comprises a total of 304 cases, whereas version 2 is more extensive,
containing 955 cases. Among these 955 cases, 655 T1-weighted MRI scans are
publicly accessible along with their ground truth, while an additional 300
T1-weighted MRI scans lack corresponding lesion masks. Preprocessing steps
applied to this dataset include intensity normalization and registration onto
the MNI-152 template. See Figure 8 for some samples from this dataset.

##  III Results and Discussion

In this section, we present the implementation details and performance
assessment metrics. Additionally, we discuss the performance of the networks
on the two datasets.

###  III-A Implementation details

The DAE-Former, LKA, and D-LKA architectures were implemented in PyTorch,
while the FCT architecture was implemented using TensorFlow. Additionally,
nnU-Net was trained using a self-adaptive method for 100 epochs, and most of
the hyperparameters for training were chosen by the framework itself. All the
results reported in this section were obtained using an NVIDIA A100 GPU with
80GB memory. The training utilized a batch size of 24 over 100 epochs with the
Stochastic Gradient Descent (SGD) optimizer. A multi-step learning rate
adjustment was used, starting at an initial rate of 0.05.

All the selected models were designed for 2D images, so we sliced the volumes
of each patient in each dataset accordingly. For the ISLES2022 dataset, we
utilized all planes - sagittal, coronal, and axial - to increase the volume of
data, as it was lower compared to the ATLAS dataset. However, for the ATLAS
dataset, we sliced the cases from the axial plane. All images were resampled
to a size of $224\times 224$.

During experiments on the ISLES 2022 dataset, we specifically utilized the DWI
modality. This decision was made because the FLAIR modality had a different
voxel spacing compared to DWI and ADC. Furthermore, we did not observe
significant performance differences when using both DWI and ADC channels
compared to using DWI alone. To enhance data diversity during training, we
applied various data augmentation techniques, including random rotation,
scaling, shear, translation, and flipping.

We experimented with various loss functions during the training of each
architecture. The best results were achieved for LKA, D-LKA, and DAE-Former
when trained with a fused loss function that combines the Dice loss and Binary
Cross Entropy (BCE) loss, with weights of 0.6 and 0.4, respectively. However,
the FCT architecture yielded the best results when trained solely on BCE loss.
It’s worth noting that D-LKA encountered challenges during training on the
ATLAS v2.0 dataset. These difficulties may stem from the high number of
parameters in this network [55], compounded by the dataset’s severe imbalance
issue, where approximately $82.17\%$ of all slices lacked stroke.
Consequently, we excluded this network from the comparison on the ATLAS
dataset.

###  III-B Performance Assessment

The Dice score (DS) serves as one of the primary evaluation metrics for
segmentation tasks, particularly in imbalanced scenarios like stroke,
providing a quantitative measure of segmentation accuracy. DS is a statistical
metric used to measure the similarity between two sets by assessing the
overlap between the predicted segmentation and the ground truth, yielding a
value between 0 and 1, where 1 indicates perfect alignment, and 0 indicates no
overlap. The DS between sets A and B is defined as:

$DS=\frac{2|A\cap B|}{|A|+|B|}=\frac{2{\times}TP}{2{\times}TP+FP+FN}$ (4)

where TP, FP, and FN represent true positive, false positive, and false
negative, respectively. One challenge encountered in utilizing DS arises from
the variety of methods available for its calculation, complicating the
comparison of results across different models and studies. To address this
issue, three distinct approaches for calculating DS were employed here, each
offering unique insights into the performance of the segmentation model. Refer
to Figure 6 for an illustration of the different ways of calculating the Dice
score.

Figure 6: Different approaches for calculating the Dice score. In the volume-
based approach, the Dice score is computed individually for each 3D volume,
and the overall Dice score is obtained by averaging across all volumes. The
aggregate approach involves tallying true positives (TP), false positives
(FP), and false negatives (FN) for each slice or case, and the final Dice
score is calculated by aggregating these values across all slices or cases.
Meanwhile, the slice-based method computes the Dice score in a 2D fashion,
where each slice is assessed independently. However, null slices (their Dice
score is shown in orange), devoid of stroke in both ground truth and predicted
labels, can significantly influence the results in this approach.

####  III-B1 Aggregated DS

An aggregated way of calculating the Dice score can be utilized for both 2D
slices and 3D volumes. This method aggregates the TP, FP, and FN values across
all cases first. Following this aggregation, the DS is computed just once at
the end of the process. Summing up the TP, FP, and FN values across all cases
captures the overall performance of the segmentation process in the entire
data set. This can be particularly beneficial in scenarios where a cumulative
assessment is more informative than individual volume assessments. However,
it’s important to note that for stroke segmentation, where there are different
sizes of stroke infarcts, this method tends to be biased towards the
performance of segmentation for large infarcts, and the performance is
typically better for larger infarcts compared to smaller ones.

####  III-B2 Slice-based DS

The second method involves calculating the Dice score for each slice
independently. This approach allows for a more detailed understanding of
segmentation performance at a single-slice level. For stroke segmentation,
where there can be different sizes in different slices, this method can offer
better insight into performance. However, it is crucial to handle null slices
carefully, those lacking stroke infarcts in both ground truth and predicted
masks. Assigning a score of 1 to null slices could potentially inflate the
average Dice score, especially in cases where only a limited number of slices
are involved with stroke. This could result in a higher Dice score even with
lower stroke segmentation performance. After calculating these individual
scores, the mean of all Dice scores is computed, providing an average measure
of segmentation accuracy throughout the entire dataset.

####  III-B3 Volume-based DS

In the third method, a 3D volume-based calculation, the Dice score is computed
separately for each volume. Subsequently, the mean of these scores is taken
across all cases. This method provides a comprehensive assessment of
segmentation performance by considering spatial relationships between voxels
within each volume. However, it has a potential drawback. As stroke
segmentation often involves multiple instances, there are cases with both
large infarcts and several small infarcts in other regions simultaneously.
This calculation method may neglect the small infarcts and yield a high Dice
score even if the small infarcts were not segmented correctly.

###  III-C Results

Table II and Table III summarize the performance of different models based on
various ways of calculating the Dice score. Figures 7 and 8 illustrate a
qualitative analysis of the performance for different cases, highlighting
various locations, sizes, and patterns of stroke infarcts.

TABLE II: Dice Scores for Different architecture using ISLES 2022 dataset; best results are shown in bold Architectures | Aggregated Dice | Slice-based Dice | Volume-based Dice
---|---|---|---
|  | With Null Slices | Without Null Slices |
DAE-Former | 0.84284 | 0.67930 | 0.47472 | 0.68694
FCT | 0.84417 | 0.78966 | 0.55605 | 0.72042
LKA | 0.84824 | 0.71638 | 0.49948 | 0.70984
D-LKA | 0.87087 | 0.69694 | 0.50327 | 0.75644
nnU-Net | 0.88649 | 0.87679 | 0.71166 | 0.80044
TABLE III: Dice Scores for Different architecture using ATLAS v2.0 dataset; best results are shown in bold Architectures | Aggregated Dice | Slice-based Dice | Volume-based Dice
---|---|---|---
|  | With Null Slices | Without Null Slices |
DAE-Former | 0.76849 | 0.73544 | 0.26141 | 0.39351
FCT | 0.80510 | 0.84771 | 0.37167 | 0.43506
LKA | 0.78569 | 0.76583 | 0.27653 | 0.40620
nnU-Net | 0.83332 | 0.90935 | 0.50935 | 0.57336
Figure 7: a) Sample of ISLES 2022 dataset, b) Performance of the DAE-Former
[40], c) Performance of the LKA [55], d) Performance of the D-LKA, [55], e)
Performance of the nnU-Net [60], f) Performance of the FCT [53]. Figure 8: a)
Sample of ATLAS v2.0 dataset, b) Performance of the DAE-Former, [40], c)
Performance of the LKA [55], d) Performance of the D-LKA [55], e) Performance
of the nnU-Net [60], f) Performance of the FCT [53].

Across both datasets, DAE-Former exhibited the weakest performance, while nnU-
Net achieved the best results, although they were still not entirely
favorable. In the comparison of Transformer-based architectures, FCT
outperformed DAE-Former by a significant margin in most Dice scores,
highlighting the importance of local information provided by CNNs for stroke
segmentation.

While Transformer-based architectures have demonstrated success in various
medical image segmentation tasks, they still struggle to achieve desirable
performance for stroke segmentation. This challenge arises from the high
variability exhibited in stroke infarcts across various features such as
morphology, size, number, location, and pattern. To investigate the potential
reasons for the performance gap between pure Transformer architectures and
hybrid or CNN-based architectures, we aimed to conduct ablation studies to
analyze the impact of data factors on the Transformer’s learning process,
which could potentially lead to weaker results.

Splitting the data based on infarct size across different slices posed a
significant challenge and resulted in a reduction in the amount of available
data. Therefore, we decided to exclude the study based on infarct size.
Another important variability in stroke segmentation is the presence of
multiple instances and the possibility of a large number of unconnected
components within the same slice. To assess the robustness of DAE-Former to
this variability, we conducted an ablation study, the results of which are
summarized in Table IV for both the ISLES 2022 and ATLAS datasets. We excluded
slices with more than 5 unconnected components from the training dataset of
ISLES 2022 and slices with more than 3 unconnected components from the
training dataset of ATLAS, while keeping the test dataset unchanged. These
thresholds were chosen based on the histogram of the number of unconnected
components in each dataset, as depicted in Figure 9. As shown in the table, by
excluding those slices, the DAE-Former network demonstrated improved
performance across all Dice scores in both datasets, despite being trained
with less data and evaluated on the same test dataset. The limited number of
slices with a high number of unconnected components resulted in a weaker
overall performance, indicating that Transformers are not robust to such
variabilities with an imbalanced distribution of data.

Figure 9: Distribution of the number of unconnected components for stroke infarcts per slice in each dataset. TABLE IV: An ablation study assessing the impact of the distribution of unconnected components in the training dataset on evaluation performance using the same dataset. In the selected training dataset, slices with a high number of unconnected components were excluded. Dataset | Aggregated Dice | Slice-based Dice | Volume-based Dice
---|---|---|---
|  | With Null Slices | Without Null Slices |
ISLES 2022 (all train data) | 0.84284 | 0.67930 | 0.47472 | 0.68694
ISLES 2022 (selected train data) | 0.84656 | 0.69261 | 0.48199 | 0.70508
ATLAS v2.0 (all train data) | 0.76849 | 0.73544 | 0.26141 | 0.39351
ATLAS v2.0 (selected train data) | 0.77815 | 0.77976 | 0.29254 | 0.40713

Adapting CNNs to capture local information, particularly texture-related
features, appears to be a more rational approach for brain stroke
segmentation. When designing hybrid CNN-Transformer architectures, careful
consideration of how to integrate Transformer-based and CNN-based blocks is
essential. Furthermore, FCT achieved better results for most Dice scores
compared to LKA and DLKA, suggesting that the global information captured by
Transformer-based structures may be more beneficial than existing methods that
modify CNN-based structures to extract global information for stroke
segmentation.

The nnU-Net framework designed a simple U-shaped architecture with adjusted
hyperparameters based on the data, as well as pre- and post-processing steps
that can enhance results. The CNN blocks used in these architectures have a
size of 3, primarily providing local information rather than global
information. The superior results obtained using the nnU-Net framework
compared to other architectures highlight the importance of preprocessing and
postprocessing steps for stroke segmentation. The complex characteristics of
stroke segmentation require careful consideration when designing automatic
pipelines. It’s worth noting that the results for stroke segmentation are
still not as advanced as those for other tasks such as brain tumor
segmentation [69], indicating the need for further refinements and
considerations in designing automated pipelines to make them applicable in
clinical settings.

In particular, the performance of all methods for all architectures was higher
for the ISLES 2022 dataset compared to the ATLAS dataset. This disparity may
be attributed to the imaging modality used in ATLAS (T1-weighted), where
stroke infarcts are less visible compared to the DWI modality. Additionally,
the imbalance issue in the ATLAS dataset was more severe than in ISLES. As
evident from the figures, all methods exhibited better performance for large
infarcts compared to small ones. For tiny infarcts where only a few pixels
were labeled as stroke in the entire slice, most architectures struggled to
segment these infarcts accurately.

Furthermore, the Aggregated Dice metric shows less variation in performance
across different architectures for this task, likely due to its bias towards
larger sizes in tasks containing variable sizes for segmentation such as
stroke segmentation. When considering null slices in calculating the Dice
score for this task, wherein many slices may lack infarction, it can lead to
an illusion of higher performance. However, this issue is mitigated when using
slice-based Dice score calculation, which excludes null slices, as well as
volume-based Dice score calculation.

##  IV Conclusion

In this study, we evaluated different deep architectures for brain stroke
segmentation in MRI images. We selected four types of networks for this
evaluation: a pure Vision Transformer-based architecture (DAE-Former), one
advanced fully convolutional Transformer (FCT), two CNN-based networks with
visual attention mechanisms in their design (LKA, D-LKA), and the widely-used
self-adaptive nnU-Net framework. All these networks have shown superior
performance in various segmentation tasks and datasets. We assessed their
performance on two publicly available datasets: ISLES 2022 and ATLAS v2.0.

Based on the results, nnU-Net outperformed other baseline methods by a large
margin, despite having the simplest design, while the pure Transformer network
(DAE-Former) exhibited the weakest performance. This suggests that the
incorporation of CNN layers in the proposed architectures for brain stroke
segmentation provides more informative features compared to the information
that Transformers can provide alone. Our evaluations indicate that local
information is more effective than global information for stroke segmentation,
likely due to the high variability in stroke shapes, sizes, locations, and
patterns that global information struggles to adequately capture for
segmentation purposes.

We also conducted an ablation study to explore the impact of the number of
unconnected components in the training dataset on the performance of the pure
Transformer architecture. This investigation aimed to understand why
Transformers exhibit weaker performance in stroke segmentation compared to
other tasks where this issue is not observed. Our study revealed that limiting
the distribution of the number of unconnected infarcts in each slice, and
excluding slices with a high number of unconnected components, led to improved
performance on the same test dataset. This suggests that Transformers are not
robust to such features, where there is an imbalanced distribution across the
dataset, with limited slices representing certain parts of the distribution.
Further analysis could involve examining the robustness of Transformer-based
architectures based on advanced morphological features, particularly for tasks
like stroke segmentation that exhibit high variability.

While our research revealed that the simplest architectural design achieved
the highest performance, it was largely attributable to the preprocessing and
post-processing steps incorporated into nnU-Net. Exploring more advanced
architectures, coupled with appropriate data processing techniques, has the
potential to significantly enhance results, given that current outcomes are
not satisfactory. For future endeavors, researchers could focus on carefully
selecting network elements based on the characteristics of stroke segmentation
to develop a potentially applicable network.

Although we carefully selected different architectural designs to examine
their performance in stroke segmentation, it is important to acknowledge the
limitations of our study. Firstly, there are numerous architectures proposed
for medical image segmentation in recent years, and we only considered some of
them. The results may not be generalizable to other architectures, whether
they are Transformer-based, hybrid, or CNN-based. Secondly, due to the time
required to train baseline models, we were unable to perform an extensive grid
search for tuning the hyperparameters. Therefore, we conducted the grid search
within a close range of the originally proposed hyperparameters. This could
affect the results, and the architectures may perform differently in other
configurations.

##  V Acknowledgment

The findings presented in this paper have emerged from a project funded by the
Qatar Japan Research Collaboration Research Program under grant number
M-QJRC-2023-313. The authors extend their sincere gratitude to Marubeni and
Qatar University for their consistent and generous support.

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