Segmentation of Ischemic Stroke Lesions using Transfer Learning on Multi-sequence MRI

• URL: http://arxiv.org/abs/2511.07281v1
• Date: Mon, 10 Nov 2025 16:27:25 GMT
• Title: Segmentation of Ischemic Stroke Lesions using Transfer Learning on Multi-sequence MRI
• Authors: R. P. Chowdhury, T. Rahman,
• Abstract summary: We present a novel framework for automatically segmenting ischemic stroke lesions on various MRI sequences.<n>The proposed methodology is validated on the ISLES 2015 Brain Stroke sequence dataset.<n>Our efforts culminated in achieving a Dice score of 80.5% and an accuracy of 74.03%, showcasing the efficacy of our segmentation approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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