Stroke Lesion Segmentation

Stroke lesion segmentation, the automated identification of stroke-damaged brain areas in medical images, aims to improve diagnostic accuracy and treatment planning. Current research heavily utilizes deep learning, employing architectures like U-Net and transformers, often enhanced with techniques such as fuzzy logic, adaptive normalization, and novel loss functions to address challenges like small lesion detection and inter-site variability in image data. These advancements offer the potential for faster, more objective stroke assessment, ultimately improving patient care and facilitating more efficient research into stroke recovery and prognosis.