@inproceedings{Wang:2019,
author = {Wang, S and Dai, C and Mo, Y and Angelini, E and Guo, Y and Bai, W},
title = {Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction},
url = {http://arxiv.org/abs/1911.08483v1},
year = {2019}
}

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TY  - CPAPER
AB  - Gliomas are the most common malignant brain tumourswith intrinsicheterogeneity. Accurate segmentation of gliomas and theirsub-regions onmulti-parametric magnetic resonance images (mpMRI)is of great clinicalimportance, which defines tumour size, shape andappearance and providesabundant information for preoperative diag-nosis, treatment planning andsurvival prediction. Recent developmentson deep learning have significantlyimproved the performance of auto-mated medical image segmentation. In thispaper, we compare severalstate-of-the-art convolutional neural network modelsfor brain tumourimage segmentation. Based on the ensembled segmentation, wepresenta biophysics-guided prognostic model for patient overall survivalpredic-tion which outperforms a data-driven radiomics approach. Our methodwonthe second place of the MICCAI 2019 BraTS Challenge for theoverall survivalprediction.
AU  - Wang,S
AU  - Dai,C
AU  - Mo,Y
AU  - Angelini,E
AU  - Guo,Y
AU  - Bai,W
PY  - 2019///
TI  - Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
UR  - http://arxiv.org/abs/1911.08483v1
ER  - 

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