Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
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DOI：
10.1016/j.media.2016.10.004
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发表时间：
2017-02-01
影响因子：
10.9
通讯作者：
Glocker, Ben
中科院分区：
文献类型：
--
作者：
Kamnitsas, Konstantinos;Ledig, Christian;Glocker, Ben
我们提出了一种双路径、11层深度的三维卷积神经网络，用于具有挑战性的脑病变分割任务。设计的体系结构是对当前为类似应用提出的网络的局限性进行深入分析的结果。为了克服处理3D医学扫描的计算负担，我们设计了一种高效且有效的密集训练方案，该方案将相邻图像块的处理加入到通过网络的一遍中，同时自动适应数据中固有的类别不平衡。进一步，我们分析了更深层的3D CNN的发展，从而更具区分性。为了同时包含局部和更大的上下文信息，我们采用了同时在多个尺度上处理输入图像的双路径体系结构。对于网络软分割的后处理，我们使用了一种3D全连通条件随机场，有效地去除了误报。我们的流程在三个具有挑战性的任务上进行了广泛的评估，这些任务是在具有创伤性脑损伤、脑肿瘤和缺血性中风的多通道MRI患者数据中进行病变分割。我们改进了所有三个应用程序的最先进水平，其中在公共基准Brats 2015和Isles 2015上的表现名列前茅。我们的方法在计算上是有效的，这使得它可以在各种研究和临床环境中采用。我们实现的源代码是公开提供的。(C)2016年提交人。爱思唯尔出版公司(Elsevier B.V.)
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available. (C) 2016 The Authors. Published by Elsevier B.V.