Transformers-based architectures for stroke segmentation: A review

TL;DR

This review explores the application of Transformer-based deep learning architectures in stroke segmentation, highlighting recent advances, challenges, and future research directions in medical image analysis.

Contribution

It provides a comprehensive analysis of Transformer architectures in stroke segmentation, including their design, performance, and limitations, which is a novel synthesis in this domain.

Findings

Transformers show promise in capturing complex spatial features in medical images.

Current methods face challenges related to computational efficiency and accuracy.

Future research should focus on optimizing Transformer models for clinical use.

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