An ensemble of 2D convolutional neural networks for tumor segmentation

Abstract

Accurate tumor segmentation plays an important role in radiosurgery planning and the assessment of radiotherapy treatment efficacy. In this paper we propose a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images. The segmentation is done in three steps; first the full tumor region, is segmented from the background by a voxel-wise merging of the decisions of three networks learned from three orthogonal planes, next the segmentation is refined using a cellular automaton-based seed growing method known as growcut. Finally, within-tumor sub-regions are segmented using an additional ensemble of networks trained for the task. We demonstrate the method on the MICCAI Brain Tumor Segmentation Challenge dataset of 2014, and show improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation without growcut. We further obtain competitive Dice scores compared with the most recent tumor segmentation challenge.