Stroke remains a significant global health concern, necessitating precise and
efficient diagnostic tools for timely intervention and improved patient
outcomes. The emergence of deep learning methodologies has transformed the
landscape of medical image analysis. Recently, Transformers, initially designed
for natural language processing, have exhibited remarkable capabilities in
various computer vision applications, including medical image analysis. This
comprehensive review aims to provide an in-depth exploration of the
cutting-edge Transformer-based architectures applied in the context of stroke
segmentation. It commences with an exploration of stroke pathology, imaging
modalities, and the challenges associated with accurate diagnosis and
segmentation. Subsequently, the review delves into the fundamental ideas of
Transformers, offering detailed insights into their architectural intricacies
and the underlying mechanisms that empower them to effectively capture complex
spatial information within medical images. The existing literature is
systematically categorized and analyzed, discussing various approaches that
leverage Transformers for stroke segmentation. A critical assessment is
provided, highlighting the strengths and limitations of these methods,
including considerations of performance and computational efficiency.
Additionally, this review explores potential avenues for future research and
development

依赖于诊断工具及深度学习方法，该论文旨在探索基于 Transformer 的创新架构在中风分割领域的应用，提供详细的分析和评估，同时探讨未来的研究发展方向。

基于 Transformer 的卒中分割架构：综述

Transformers-based architectures for stroke segmentation: A review

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: a pure Transformer-based architecture
(DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention
mechanisms in their design, an advanced hybrid model that incorporates CNNs
with Transformers (FCT), and the well- known self-adaptive nnUNet framework
with its configuration based on given data. We examined their performance on
two publicly available datasets, and found that the nnUNet achieved the best
results with the simplest design among all. Revealing the robustness issue of
Transformers to such variabilities serves as a potential reason for their
weaker performance. Furthermore, nnUNet's success underscores the significant
impact of preprocessing and postprocessing techniques in enhancing segmentation
results, surpassing the focus solely on architectural designs

本研究选择了四种深度模型进行中风分割，即纯 Transformer 结构 (DAE-Former)，两种带有注意机制的先进 CNN 模型 (LKA 和 DLKA)，一种将 CNN 与 Transformer 结合的高级混合模型 (FCT)，以及以给定数据为基础的自适应 nnUNet 框架，并在两个公开可用的数据集上进行了评估，结果表明 nnUNet 在所有模型中具有最简单的设计，并通过强调预处理和后处理技术来提高分割结果，超越了仅关注架构设计的重要性。