Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
This work addresses the problem of accurate glioma segmentation and survival prediction for clinical applications, representing an incremental improvement with a novel hybrid approach.
The paper tackled brain tumor segmentation and survival prediction by comparing state-of-the-art CNN models for segmentation and introducing a biophysics-guided prognostic model, which achieved second place in the MICCAI 2019 BraTS Challenge for survival prediction.
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images (mpMRI)is of great clinical importance, which defines tumour size, shape andappearance and provides abundant information for preoperative diag-nosis, treatment planning and survival prediction. Recent developmentson deep learning have significantly improved the performance of auto-mated medical image segmentation. In this paper, we compare severalstate-of-the-art convolutional neural network models for brain tumourimage segmentation. Based on the ensembled segmentation, we presenta biophysics-guided prognostic model for patient overall survival predic-tion which outperforms a data-driven radiomics approach. Our methodwon the second place of the MICCAI 2019 BraTS Challenge for theoverall survival prediction.