Brain Tumor Segmentation and Survival Prediction

TL;DR

This paper presents a deep learning approach for glioma segmentation in brain MRI scans and combines it with radiomic features to predict patient survival, achieving promising accuracy on the BraTS 2019 dataset.

Contribution

It introduces a novel encoder-decoder neural network architecture with dense connections for tumor segmentation and integrates radiomic features for survival prediction.

Findings

Dice scores of 0.92, 0.90, and 0.79 for tumor segmentation.

55.4% classification accuracy for survival prediction.

Effective segmentation and survival classification on BraTS 2019 data.

Abstract

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.