# 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction

arXiv: https://arxiv.org/abs/2008.11576 · HTML: https://mlsanity.com/paper/2008.11576/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis](https://mlsanity.com/paper/2210.13336/) (2022)
- [Glioblastoma Overall Survival Prediction With Vision Transformers](https://mlsanity.com/paper/2508.02439/) (2025)
- [Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images](https://mlsanity.com/paper/2401.01303/) (2024)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis](https://mlsanity.com/paper/2602.15067/) (2026)
- [BrainAid: Efficient Brain Tumor Detection Using Lightweight CNNs and Explainable AI for Resource-Constrained Environments](https://mlsanity.com/paper/2610.15077/) (2026)

## Abstract (arXiv)

Glioma, the malignant brain tumor, requires immediate treatment to improve the survival of patients. Gliomas heterogeneous nature makes the segmentation difficult, especially for sub-regions like necrosis, enhancing tumor, non-enhancing tumor, and Edema. Deep neural networks like full convolution neural networks and ensemble of fully convolution neural networks are successful for Glioma segmentation. The paper demonstrates the use of a 3D fully convolution neural network with a three layer encoder decoder approach for layer arrangement. The encoder blocks include the dense modules, and decoder blocks include convolution modules. The input to the network is 3D patches. The loss function combines dice loss and focal loss functions. The validation set dice score of the network is 0.74, 0.88, and 0.73 for enhancing tumor, whole tumor, and tumor core, respectively. The Random Forest Regressor uses shape, volumetric, and age features extracted from ground truth for overall survival prediction. The regressor achieves an accuracy of 44.8% on the validation set.