3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction

TL;DR

This paper presents a 3D fully convolutional neural network for segmenting glioma sub-regions in brain MRI scans and uses extracted features for predicting patient survival, demonstrating promising results.

Contribution

It introduces a novel 3D encoder-decoder CNN architecture with combined loss functions for improved glioma segmentation and applies feature-based regression for survival prediction.

Findings

Dice scores of 0.74, 0.88, and 0.73 for tumor sub-regions.

Survival prediction accuracy of 44.8%.

Effective combination of segmentation and survival modeling.

Abstract

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.