Multi-planar Spatial-ConvNet for Segmentation and Survival Prediction in Brain Cancer

Abstract

A new deep learning method is introduced for the automatic delineation/segmentation of brain tumors from multi-sequence MR images. A Radiomic model for predicting the Overall Survival (OS) is designed, based on the features extracted from the segmented Volume of Interest (VOI). An encoder-decoder type ConvNet model is designed for pixel-wise segmentation of the tumor along three anatomical planes (axial, sagittal and coronal) at the slice level. These are then combined, using a consensus fusion strategy, to produce the final volumetric segmentation of the tumor and its sub-regions. Novel concepts such as spatial-pooling and unpooling are introduced to preserve the spatial locations of the edge pixels for reducing segmentation error around the boundaries. We also incorporate shortcut connections to copy and concatenate the receptive fields from the encoder to the decoder part, for helping the decoder network localize and recover the object details more effectively. These connections allow the network to simultaneously incorporate high-level features along with pixel-level details. A new aggregated loss function helps in effectively handling data imbalance. The integrated segmentation and OS prediction system is trained and validated on the BraTS 2018 dataset.