Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction

TL;DR

This paper introduces a multi-resolution 3D CNN architecture for automated brain tumor segmentation and combines it with a random forest classifier for survival prediction, achieving promising results on BraTS 2019 data.

Contribution

It proposes a novel multi-resolution 3D CNN architecture that considers whole images for improved segmentation and integrates survival prediction using random forests.

Findings

Dice scores of 0.84, 0.74, 0.71 for validation set

Dice scores of 0.82, 0.72, 0.70 for challenge test set

Survival prediction accuracy of 52% (validation), 49% (test)

Abstract

In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classi-fication algorithm based on random forests, for survival prediction is presented. The objective is to segment the glioma area and produce segmentation labels for its different sub-regions, i.e. necrotic and the non-enhancing tumor core, the peri-tumoral edema, and enhancing tumor. The proposed deep architecture for the segmentation task encompasses two parallel streamlines with two different reso-lutions. One deep convolutional neural network is to learn local features of the input data while the other one is set to have a global observation on whole image. Deemed to be complementary, the outputs of each stream are then merged to pro-vide an ensemble complete learning of the input image. The proposed network takes the whole image as input instead of patch-based approaches in order to con-sider the semantic features throughout the whole volume. The algorithm is trained on BraTS 2019 which included 335 training cases, and validated on 127 unseen cases from the validation dataset using a blind testing approach. The proposed method was also evaluated on the BraTS 2019 challenge test dataset of 166 cases. The results show that the proposed methods provide promising segmentations as well as survival prediction. The mean Dice overlap measures of automatic brain tumor segmentation for validation set were 0.84, 0.74 and 0.71 for the whole tu-mor, core and enhancing tumor, respectively. The corresponding results for the challenge test dataset were 0.82, 0.72, and 0.70, respectively. The overall accura-cy of the proposed model for the survival prediction task is %52 for the valida-tion and %49 for the test dataset.