  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
