Transformers-based architectures for stroke segmentation: A review

TL;DR

This comprehensive review aims to provide an in-depth exploration of the cutting-edge Transformer-based architectures applied in the context of stroke segmentation, offering detailed insights into their architectural intricacies and the underlying mechanisms that empower them to effectively capture complex spatial information within medical images.

Abstract

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

Figures (11)

• Figure 1: Sample of stroke infarct on Diffusion-weighted and Apparent diffusion coefficient MRI with the annotation, from hernandez2022isles. The infarct appears hyperintense in DWI and hypointense in ADC.
• Figure 2: Sample of stroke infarct on CT images with the annotation, from cereda2016benchmarkinghakim2021predicting.
• Figure 3: Overview of key aspects covered in this review paper.
• Figure 4: Overview of Vision Transformer (ViT) and the Transformer encoder.
• Figure 5: The cyclic shift of the local window for Shifted Windows-based self-attention computation. The self-attention is computed in each local window.