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2021 | OriginalPaper | Chapter

A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival Prediction

Authors : Radu Miron, Ramona Albert, Mihaela Breaban

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

Publisher: Springer International Publishing

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Abstract

Glioma is a type of heterogeneous tumor originating in the brain, characterized by the coexistence of multiple subregions with different phenotypic characteristics, which further determine heterogeneous profiles, likely to respond variably to treatment. Identifying spatial variations of gliomas is necessary for targeted therapy. The current paper proposes a neural network composed of heterogeneous building blocks to identify the different histologic sub-regions of gliomas in multi-parametric MRIs and further extracts radiomic features to estimate a patient’s prognosis. The model is evaluated on the BraTS 2020 dataset.

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previous chapter A Deep Supervised U-Attention Net for Pixel-Wise Brain Tumor Segmentation

next chapter TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI Data

https://github.com/IBM/pytorch-large-model-support.

https://github.com/maduriron/BraTS2020.

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Title: A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival Prediction
Authors: Radu Miron
Ramona Albert
Mihaela Breaban
Publisher: Springer International Publishing
Book: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Print ISBN: 978-3-030-72086-5

Electronic ISBN: 978-3-030-72087-2

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-72087-2_25

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