乐胖代购免代理版

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However, the manual segmentation of the MRI image is strenuous. With the development of deep learning, a large number of automatic segmentation methods have been developed, but most of them stay in 2D images, which leads to subpar performance. Aiming at segmenting 3D MRI, we propose a model for brain tumor segmentation with multiple encoders. Our model reduces the difficulty of feature extraction and greatly improves model performance. We also introduced a new loss function named \u201cCategorical Dice,\u201d and set different weights for different segmented regions at the same time, which solved the problem of voxel imbalance. We evaluated our approach using the online BraTS 2020 Challenge verification. Our proposed method can achieve promising results compared to the state\u2010of\u2010the\u2010art approaches with Dice scores of 0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhancing tumor.<\/jats:p>","DOI":"10.1002\/ima.22571","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T05:31:44Z","timestamp":1615181504000},"page":"1834-1848","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["ME\u2010Net<\/scp>: Multi\u2010encoder<\/scp> net framework for brain tumor segmentation"],"prefix":"10.1002","volume":"31","author":[{"given":"Wenbo","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Medical Technology Zhejiang Chinese Medical University Hangzhou China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7344-7733","authenticated-orcid":false,"given":"Guang","family":"Yang","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Centre Royal Brompton Hospital London UK"},{"name":"National Heart and Lung Institute Imperial College London London UK"}]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Medical Technology Zhejiang Chinese Medical University Hangzhou China"}]},{"given":"Weiji","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Life Science Zhejiang Chinese Medical University Hangzhou China"}]},{"given":"Xiaomei","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Medical Technology Zhejiang Chinese Medical University Hangzhou China"}]},{"given":"Yongkai","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, David Geffen School of Medicine University of California at Los Angeles Los Angeles California USA"}]},{"given":"Xiaobo","family":"Lai","sequence":"additional","affiliation":[{"name":"College of Medical Technology Zhejiang Chinese Medical University Hangzhou China"}]}],"member":"311","published-online":{"date-parts":[[2021,3,7]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"BakasS ReyesM JakabA et al.Identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629; 2018."},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.12.032"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2538465"},{"key":"e_1_2_11_5_1","doi-asserted-by":"crossref","unstructured":"MyronenkoA.3D MRI brain tumor segmentation using autoencoder regularization.Lecture Notes in Computer Science. 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