# Brain Tumor Segmentation and Survival Prediction

arXiv: https://arxiv.org/abs/1909.09399 · HTML: https://mlsanity.com/paper/1909.09399/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities](https://mlsanity.com/paper/2104.06231/) (2021)
- [Post-hoc Overall Survival Time Prediction from Brain MRI](https://mlsanity.com/paper/2102.10765/) (2021)
- [Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images](https://mlsanity.com/paper/2205.13226/) (2022)
- [ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2401.14074/) (2024)
- [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)
- [A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction](https://mlsanity.com/paper/2101.10599/) (2021)
- [Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images](https://mlsanity.com/paper/2109.12334/) (2021)
- [Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning](https://mlsanity.com/paper/2406.08634/) (2024)
- [No Modality Left Behind: Dynamic Model Generation for Incomplete Medical Data](https://mlsanity.com/paper/2509.11406/) (2025)

## Abstract (arXiv)

The paper demonstrates the use of the fully convolutional neural network for glioma segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture is used along with dense connection at encoder part to propagate the information from coarse layer to deep layers. This architecture is used to train three tumor sub-components separately. Subcomponent training weights are initialized with whole tumor weights to get the localization of the tumor within the brain. At the end, three segmentation results were merged to get the entire tumor segmentation. Dice Similarity of training dataset with focal loss implementation for whole tumor, tumor core and enhancing tumor is 0.92, 0.90 and 0.79 respectively. Radiomic features along with segmentation results and age are used to predict the overall survival of patients using random forest regressor to classify survival of patients in long, medium and short survival classes. 55.4% of classification accuracy is reported for training dataset with the scans whose resection status is gross-total resection.