Ischemic Stroke Lesion Segmentation

Ischemic stroke lesion segmentation aims to automatically identify and delineate the affected brain regions in medical images, primarily using MRI and CT scans, to aid in diagnosis, treatment planning, and prognosis. Current research heavily utilizes deep learning models, such as U-Net and its variants, along with ensemble methods and novel training strategies like multi-size and distance-based labeling, to improve segmentation accuracy, particularly for small lesions and across diverse datasets. This work is crucial for improving the efficiency and accuracy of stroke diagnosis and treatment, potentially leading to better patient outcomes and advancing the clinical application of AI in neuroimaging.