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• W2899013683 abstract "Acute stroke lesion segmentation and prediction tasks are of great clinicalinterest as they can help doctors make better informed time-critical treatmentdecisions. Automatic segmentation of these lesions is a complex task due totheir heterogeneous appearance, dynamic evolution and inter-patientdifferences. Typically, acute stroke lesion tasks are approached with methodsdeveloped for chronic stroke or other brain lesions. However, thepathophysiology and anatomy of acute stroke establishes an inherently differentproblem that needs special consideration. In this work, we propose a novel deeplearning architecture specially designed for acute stroke tasks that involveapproximating complex non-linear functions with reduced data. Within ourstrategy, class imbalance is tackled using a hybrid strategy based onstate-of-the-art train sampling strategies designed for other brain lesionrelated tasks, which is more suited to the anatomy and pathophysiology of acutestroke lesions. The proposed method is evaluated on three unrelated publicinternational challenge datasets (ISLES) without any dataset specifichyper-parameter tuning. These involve the tasks of sub-acute stroke lesionsegmentation, acute stroke penumbra estimation and chronic extent predictionfrom acute MR images. The performance of the proposed architecture is analysedboth against similar deep learning architectures from chronic stroke andrelated biomedical tasks and also by submitting the segmented test images forblind online evaluation on each of the challenges. When compared with the restof submitted strategies, our method achieves top-rank performance among thebest submitted entries in all the three challenges, showing its capability todeal with different unrelated tasks without hyper-parameter tuning. In order topromote the reproducibility of our results, a public version of the proposedmethod has been released." @default.
• W2899013683 created "2018-11-09" @default.
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• W2899013683 date "2018-10-31" @default.
• W2899013683 modified "2023-10-18" @default.
• W2899013683 title "SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI." @default.
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