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2021 | OriginalPaper | Buchkapitel

Cascaded Coarse-to-Fine Neural Network for Brain Tumor Segmentation

verfasst von : Shuojue Yang, Dong Guo, Lu Wang, Guotai Wang

Erschienen in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Verlag: Springer International Publishing

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Abstract

A cascaded framework of coarse-to-fine networks is proposed to segment brain tumor from multi-modality MR images into three subregions: enhancing tumor, whole tumor and tumor core. The framework is designed to decompose this multi-class segmentation into two sequential tasks according to hierarchical relationship among these regions. In the first task, a coarse-to-fine model based on Global Context Network predicts segmentation of whole tumor, which provides a bounding box of all three substructures to crop the input MR images. In the second task, cropped multi-modality MR images are fed into another two coarse-to-fine models based on NvNet trained on small patches to generate segmentation of tumor core and enhancing tumor, respectively. Experiments with BraTS 2020 validation set show that the proposed method achieves average Dice scores of 0.8003, 0.9123, 0.8630 for enhancing tumor, whole tumor and tumor core, respectively. The corresponding values for BraTS 2020 testing set were 0.81715, 0.88229, 0.83085, respectively.

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Vorheriges Kapitel Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

Nächstes Kapitel Low-Rank Convolutional Networks for Brain Tumor Segmentation

http://www.med.upenn.edu/cbica/brats2020.html.

https://www.cbica.upenn.edu/BraTS20/lboardValidation.html.

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Titel: Cascaded Coarse-to-Fine Neural Network for Brain Tumor Segmentation
verfasst von: Shuojue Yang
Dong Guo
Lu Wang
Guotai Wang
Verlag: Springer International Publishing
Buch: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Print ISBN: 978-3-030-72083-4

Electronic ISBN: 978-3-030-72084-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-72084-1_41

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