VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.
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DOI：
10.1016/j.neuroimage.2017.04.041
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发表时间：
2018-04-15
期刊：
影响因子：
5.7
通讯作者：
Heng, Pheng-Ann
中科院分区：
文献类型：
--
作者：
Chen, Hao;Dou, Qi;Heng, Pheng-Ann
从三维医学图像中分割关键脑组织对脑部疾病诊断、进展评估和神经系统状况监测具有重要意义。人工分割耗时、费力、主观，而由于大脑复杂的解剖环境和脑组织的巨大变化，自动分割具有很大的挑战性。我们提出了一种新的体素残差网络（VoxResNet）和一套有效的训练方案来应对这一具有挑战性的问题。残差学习的主要优点是它可以缓解深度网络训练时的退化问题，从而充分利用增加网络深度所获得的性能增益。使用这种技术，我们的VoxResNet由25层构建，因此可以生成更多具有代表性的特征来处理脑组织的大变化，而不是使用手工制作的特征或较浅的网络。为了在训练数据有限的情况下有效地训练这样的深度脑分割网络，我们将多模态和多层次的上下文信息无缝集成到网络中，从而利用不同模态的互补信息，利用不同尺度的特征。在此基础上，提出了一种自动上下文版本的VoxResNet，将底层图像外观特征、隐式形状信息和高层上下文相结合，进一步提高了分割性能。在3D磁共振（MR）图像中进行脑分割的著名基准（即MRBrainS）上进行的大量实验证实了所提出的VoxResNet的有效性。我们的方法在包括几种最先进的大脑分割方法在内的37个竞争者中获得了第一名。我们的方法本质上是通用的，可以很容易地作为一个强大的工具应用于许多与大脑相关的研究，其中大脑结构的准确分割是至关重要的。
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.