TPCNN: Two-phase patch-based convolutional neural network for automatic brain tumor segmentation and survival prediction

Abstract

The aim of this paper is to integrate some advanced statistical methods with modern deep learning methods for tumor segmentation and survival time prediction in the BraTS 2017 challenge. The goals of the BraTS 2017 challenge are to utilize multi-institutional pre-operative MRI scans to segment out different tumor subregions and then to use tumor information to predict patient’s overall survival. We build a two-phase patch-based convolutional neural network (TPCNN) model to classify all the pixels in the brain and further refine the segmentation results by using XGBoost and a post-processing procedure. The segmentation results are then used to extract various informative radiomic features for prediction of the survival time by using the XGBoost method.