{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sunet-a-deep-learning-architecture-for-acute","title":"Acute and sub-acute stroke lesion segmentation from multimodal MRI","arxiv_id":"1810.13304","date":"2018-10-31","proceeding":null,"authors":["Albert Clèrigues","Sergi Valverde","Jose Bernal","Jordi Freixenet","Arnau Oliver","Xavier Lladó"],"abstract":"Acute stroke lesion segmentation tasks are of great clinical interest as they\ncan help doctors make better informed treatment decisions. Magnetic resonance\nimaging (MRI) is time demanding but can provide images that are considered gold\nstandard for diagnosis. Automated stroke lesion segmentation can provide with\nan estimate of the location and volume of the lesioned tissue, which can help\nin the clinical practice to better assess and evaluate the risks of each\ntreatment. We propose a deep learning methodology for acute and sub-acute\nstroke lesion segmentation using multimodal MR imaging. The proposed method is\nevaluated using two public datasets from the 2015 Ischemic Stroke Lesion\nSegmentation challenge (ISLES 2015). These involve the tasks of sub-acute\nstroke lesion segmentation (SISS) and acute stroke penumbra estimation (SPES)\nfrom diffusion, perfusion and anatomical MRI modalities. The performance is\ncompared against state-of-the-art methods with a blind online testing set\nevaluation on each of the challenges. At the time of submitting this\nmanuscript, our approach is the first method in the online rankings for the\nSISS (DSC=0.59$\\pm$0.31) and SPES sub-tasks (DSC=0.84$\\pm$0.10). When compared\nwith the rest of submitted strategies, we achieve top rank performance with a\nlower Hausdorff distance. Better segmentation results are obtained by\nleveraging the anatomy and pathophysiology of acute stroke lesions and using a\ncombined approach to minimize the effects of class imbalance. The same training\nprocedure is used for both tasks, showing the proposed methodology can\ngeneralize well enough to deal with different unrelated tasks and imaging\nmodalities without training hyper-parameter tuning. A public version of the\nproposed method has been released to the scientific community at\nhttps://github.com/NIC-VICOROB/stroke-mri-segmentation.","url_abs":"http://arxiv.org/abs/1810.13304v2","url_pdf":"http://arxiv.org/pdf/1810.13304v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sunet-a-deep-learning-architecture-for-acute","repo_url":"https://github.com/NIC-VICOROB/SUNet-architecture","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"sunet-a-deep-learning-architecture-for-acute","repo_url":"https://github.com/NIC-VICOROB/stroke-mri-segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"acute-stroke-lesion-segmentation","task_name":"Acute Stroke Lesion Segmentation"},{"task_slug":"anatomy","task_name":"Anatomy"},{"task_slug":"ischemic-stroke-lesion-segmentation","task_name":"Ischemic Stroke Lesion Segmentation"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"mri-segmentation","task_name":"MRI segmentation"},{"task_slug":"outcome-prediction-in-multimodal-mri","task_name":"Outcome Prediction In Multimodal Mri"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}