{"ID":2844068,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07281","arxiv_id":"2511.07281","title":"Segmentation of Ischemic Stroke Lesions using Transfer Learning on Multi-sequence MRI","abstract":"The accurate understanding of ischemic stroke lesions is critical for efficient therapy and prognosis of stroke patients. Magnetic resonance imaging (MRI) is sensitive to acute ischemic stroke and is a common diagnostic method for stroke. However, manual lesion segmentation performed by experts is tedious, time-consuming, and prone to observer inconsistency. Automatic medical image analysis methods have been proposed to overcome this challenge. However, previous approaches have relied on hand-crafted features that may not capture the irregular and physiologically complex shapes of ischemic stroke lesions. In this study, we present a novel framework for quickly and automatically segmenting ischemic stroke lesions on various MRI sequences, including T1-weighted, T2-weighted, DWI, and FLAIR. The proposed methodology is validated on the ISLES 2015 Brain Stroke sequence dataset, where we trained our model using the Res-Unet architecture twice: first, with pre-existing weights, and then without, to explore the benefits of transfer learning. Evaluation metrics, including the Dice score and sensitivity, were computed across 3D volumes. Finally, a Majority Voting Classifier was integrated to amalgamate the outcomes from each axis, resulting in a comprehensive segmentation method. Our efforts culminated in achieving a Dice score of 80.5\\% and an accuracy of 74.03\\%, showcasing the efficacy of our segmentation approach.","short_abstract":"The accurate understanding of ischemic stroke lesions is critical for efficient therapy and prognosis of stroke patients. Magnetic resonance imaging (MRI) is sensitive to acute ischemic stroke and is a common diagnostic method for stroke. However, manual lesion segmentation performed by experts is tedious, time-consumi...","url_abs":"https://arxiv.org/abs/2511.07281","url_pdf":"https://arxiv.org/pdf/2511.07281v1","authors":"[\"R. P. Chowdhury\",\"T. Rahman\"]","published":"2025-11-10T16:27:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}