Apr 27, 2017

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

NeuroImage
Hao ChenPheng-Ann Heng

Abstract

Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and f...Continue Reading

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Mentioned in this Paper

Study
Biologic Segmentation
Brain Diseases
Diagnostic Radiology Modality
Magnetic Resonance Imaging
Three-dimensional
Brain
Environment
Procedure on Brain
Evaluation

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