{"title":"基于ISLES 2018数据集的脑卒中病灶三维U-Net分割","authors":"A. Tursynova, B. Omarov","doi":"10.1109/icecco53203.2021.9663825","DOIUrl":null,"url":null,"abstract":"Brain stroke is one of the global problems today. An image such as a CT scan helps to visually see the whole picture of the brain. Segmentation of the affected brain regions requires a qualified specialist. However, manual segmentation requires a lot of time and a good expert. The role and support of trained neural networks for segmentation tasks is considered as one of the best assistants. Among the neural network models, the models based on U-Net are recognized as the leading ones. The U-Net architecture can work with a small number of datasets and is considered advanced for the segmentation method. In this paper, we use the classical U-Net architecture for the experiment. As datasets, we use 3D computed tomography images of the brain taken from ISLES 2018 the public domain. Using the classical U-Net architecture, we found that U-Net is considered the best architecture for segmentation methods. This study presents experiment results of 3D U-Net model for brain stroke lesion segmentation, and gives future perspectives for researchers who is going to segment brain strokes and create modified U-Net model for improvement. The developed model is useful for brain stroke segmentation when there is little number of images for train and testing the model.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"3D U-Net for brain stroke lesion segmentation on ISLES 2018 dataset\",\"authors\":\"A. Tursynova, B. Omarov\",\"doi\":\"10.1109/icecco53203.2021.9663825\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Brain stroke is one of the global problems today. An image such as a CT scan helps to visually see the whole picture of the brain. Segmentation of the affected brain regions requires a qualified specialist. However, manual segmentation requires a lot of time and a good expert. The role and support of trained neural networks for segmentation tasks is considered as one of the best assistants. Among the neural network models, the models based on U-Net are recognized as the leading ones. The U-Net architecture can work with a small number of datasets and is considered advanced for the segmentation method. In this paper, we use the classical U-Net architecture for the experiment. As datasets, we use 3D computed tomography images of the brain taken from ISLES 2018 the public domain. Using the classical U-Net architecture, we found that U-Net is considered the best architecture for segmentation methods. This study presents experiment results of 3D U-Net model for brain stroke lesion segmentation, and gives future perspectives for researchers who is going to segment brain strokes and create modified U-Net model for improvement. The developed model is useful for brain stroke segmentation when there is little number of images for train and testing the model.\",\"PeriodicalId\":331369,\"journal\":{\"name\":\"2021 16th International Conference on Electronics Computer and Computation (ICECCO)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 16th International Conference on Electronics Computer and Computation (ICECCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icecco53203.2021.9663825\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icecco53203.2021.9663825","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
3D U-Net for brain stroke lesion segmentation on ISLES 2018 dataset
Brain stroke is one of the global problems today. An image such as a CT scan helps to visually see the whole picture of the brain. Segmentation of the affected brain regions requires a qualified specialist. However, manual segmentation requires a lot of time and a good expert. The role and support of trained neural networks for segmentation tasks is considered as one of the best assistants. Among the neural network models, the models based on U-Net are recognized as the leading ones. The U-Net architecture can work with a small number of datasets and is considered advanced for the segmentation method. In this paper, we use the classical U-Net architecture for the experiment. As datasets, we use 3D computed tomography images of the brain taken from ISLES 2018 the public domain. Using the classical U-Net architecture, we found that U-Net is considered the best architecture for segmentation methods. This study presents experiment results of 3D U-Net model for brain stroke lesion segmentation, and gives future perspectives for researchers who is going to segment brain strokes and create modified U-Net model for improvement. The developed model is useful for brain stroke segmentation when there is little number of images for train and testing the model.