# Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction

arXiv: https://arxiv.org/abs/1911.08483 · HTML: https://mlsanity.com/paper/1911.08483/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Post-hoc Overall Survival Time Prediction from Brain MRI](https://mlsanity.com/paper/2102.10765/) (2021)
- [Glioblastoma Multiforme Patient Survival Prediction](https://mlsanity.com/paper/2101.10589/) (2021)
- [Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review](https://mlsanity.com/paper/2103.13588/) (2021)
- [No Modality Left Behind: Dynamic Model Generation for Incomplete Medical Data](https://mlsanity.com/paper/2509.11406/) (2025)

## Abstract (arXiv)

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.