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

	VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.
	Chen H; Dou Q; Yu L; Qin J; Heng PA
刊名	Neuroimage.
	2017
文献子类	期刊论文
英文摘要	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 features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain structures is critical.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/11598]
专题	深圳先进技术研究院_集成所
作者单位	Neuroimage.
推荐引用方式 GB/T 7714	Chen H,Dou Q,Yu L,et al. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.[J]. Neuroimage.,2017.
APA	Chen H,Dou Q,Yu L,Qin J,&Heng PA.(2017).VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images..Neuroimage..
MLA	Chen H,et al."VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.".Neuroimage. (2017).