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2021 | OriginalPaper | Buchkapitel

Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network

verfasst von : Hieu T. Nguyen, Tung T. Le, Thang V. Nguyen, Nhan T. Nguyen

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

Verlag: Springer International Publishing

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Abstract

Brain tumor segmentation plays an essential role in medical image analysis. In recent studies, deep convolution neural networks (DCNNs) are extremely powerful to tackle tumor segmentation tasks. We propose in this paper a novel training method that enhances the segmentation results by adding an additional classification branch to the network. The whole network was trained end-to-end on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. On the BraTS’s test set, it achieved an average Dice score of \(80.57\%\), \(85.67\%\) and \(82.00\%\), as well as Hausdorff distances \((95\%)\) of 14.22, 7.36 and 23.27, respectively for the enhancing tumor, the whole tumor and the tumor core.

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Vorheriges Kapitel Segmentation then Prediction: A Multi-task Solution to Brain Tumor Segmentation and Survival Prediction

Nächstes Kapitel Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction

https://www.med.upenn.edu/cbica/brats2020/data.html.

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Titel: Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network
verfasst von: Hieu T. Nguyen
Tung T. Le
Thang V. Nguyen
Nhan T. Nguyen
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_45

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