Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction

2019/11/19 by Shuo Wang, Chengliang Dai, Wang, Shuo +9 · 1 citation
Medicine · Neuroscience · #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Glioma Diagnosis and Treatment #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1911.08483

openalex publication_date 2019/11/19 · openalex created_date 2019/12/05 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
• Computational Radiomics System to Decode the Radiographic Phenotype

Cited by

• No Modality Left Behind: Dynamic Model Generation for Incomplete Medical Data

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