Medical Imaging 2021: Computer-Aided Diagnosis 2021
DOI: 10.1117/12.2581118
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Fully automated segmentation of brain tumor from multiparametric MRI using 3D context u-net with deep supervision

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Cited by 3 publications
(2 citation statements)
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“…Their findings correspond to 0.75, 0.87, and 0.76 as dice score values for the necrotic (core tumor), Edema (total tumor), and augmenting tumors. M. Lin et al [20] produced a 3D Context U-Net model employing a deep supervision learning model to automate brain tumor segmentation. This segmentation was performed using Multiparametric MRI data from BraTS 2019.…”
Section: Literature Reviewmentioning
confidence: 99%
See 1 more Smart Citation
“…Their findings correspond to 0.75, 0.87, and 0.76 as dice score values for the necrotic (core tumor), Edema (total tumor), and augmenting tumors. M. Lin et al [20] produced a 3D Context U-Net model employing a deep supervision learning model to automate brain tumor segmentation. This segmentation was performed using Multiparametric MRI data from BraTS 2019.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The dice scores for the Necrotic, Edema, and Enhancing tumors were 0.75, 0.87, and 0.76, respectively. In addition, Lin et al [20] proposed a 3D Context U-Net model that employs deep supervision learning to automate the segmenting of brain tumors. The segmentation was conducted via MRI data obtained from BraTS 2019.…”
Section: Comparative Analysismentioning
confidence: 99%