Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

TL;DR

This paper presents a new approach combining brain connectomics, parcellation, and deep learning to improve brain tumor segmentation and predict patient survival using MR images, leveraging the Human Connectome Project data.

Contribution

It introduces a novel method integrating connectomics and parcellation data with deep neural networks for tumor segmentation and survival prediction.

Findings

Achieved accurate tumor segmentation using deep neural networks with hard negative mining.

Developed tractographic features from connectome data that correlate with survival outcomes.

Validated methods on the BraTS2018 dataset with promising results.

Abstract

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.