Batyrkhan Omarov1,2,3, Azhar Tursynova1,*, Octavian Postolache4, Khaled Gamry5, Aidar Batyrbekov5, Sapargali Aldeshov6,7, Zhanar Azhibekova9, Marat Nurtas5,8, Akbayan Aliyeva6, Kadrzhan Shiyapov10,11
CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4701-4717, 2022, DOI:10.32604/cmc.2022.020998
Abstract The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the models for solving this problem using machine learning methods are far from ideal. In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images. We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model. Interpretation of the obtained results, as well as the ideas for further experiments are included in the… More >
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