Stroke Segmentation

Stroke segmentation, the automated identification of stroke-affected brain regions in medical images, aims to improve diagnostic accuracy and treatment planning by providing precise spatial information about lesion extent. Current research heavily utilizes deep learning, focusing on convolutional neural networks (CNNs), transformers, and hybrid architectures that combine both, with a recent emphasis on optimizing pre- and post-processing techniques alongside model design. Improved segmentation accuracy through these methods holds significant potential for faster and more reliable stroke diagnosis, ultimately leading to better patient outcomes and advancements in stroke research.