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Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images

Computers, Materials & Continua 2022年第6期71卷 4701-4717页

作者： Batyrkhan Omarov Azhar Tursynova Octavian Postolache khaled gamry Aidar Batyrbekov Sapargali Aldeshov Zhanar Azhibekova Marat Nurtas Akbayan Aliyeva Kadrzhan Shiyapov Al-Farabi Kazakh National University AlmatyKazakhstan International University of Tourism and Hospitality TurkistanKazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University TurkistanKazakhstan Instituto Universitário de Lisboa LisbonPortugal International Information Technology University AlmatyKazakhstan South Kazakhstan State Pedagogical University ShymkentKazakhstan M.Auezov South Kazakhstan University ShymkentKazakshtan Kazakh-British Technical University AlmatyKazakhstan Asfendiyarov Kazakh National Medical University AlmatyKazakhstan Kazakh National Women’s Teacher Training University AlmatyKazakhstan Abai Kazakh National Pedagogical University AlmatyKazakhstan

The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging ***,at the moment,the models for solving this problem using machine learning methods are far from *** this paper,we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3Dcomputed tomography *** use the ISLES 2018(Ischemic Stroke Lesion Segmentation Challenge 2018)open dataset to train and test the proposed *** of the obtained results,as well as the ideas for further experiments are included in the *** evaluation is performed using the Dice or f1 score coefficient and the Jaccard *** architecture may simply be extended to ischemia segmentation and computed tomography image identification by selecting relevant *** Dice/f1 score similarity coefficient of our model shown58%and results close to ground truth which is higher than the standard 3D UNet model,demonstrating that our model can accurately segment ischemic *** modified 3D UNet model proposed by us uses an efficient averaging method inside a neural *** this set of ISLES is limited in number,using the data augmentation method and neural network regularization methods to prevent overfitting gave the best *** addition,one of the advantages is the use of the Intersection over Union loss function,which is based on the assessment of the coincidence of the shapes of the recognized zones.

关键词： Stroke ischemic stroke UNet deep learning segmentation ISLES 2018

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