Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative
Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative
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DOI：
10.1016/j.media.2018.11.009
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发表时间：
2019-02-01
影响因子：
10.9
通讯作者：
Zachow, Stefan
中科院分区：
文献类型：
--
作者：
Ambellan, Felix;Tack, Alexander;Zachow, Stefan
我们提出了一种从磁共振成像（MRI）中自动分割膝盖骨和软骨的方法，该方法将解剖形状的先验知识与卷积神经网络（CNN）相结合。所提出的方法结合了3D统计形状模型（SSM）以及2D和3D CNN，以实现对高度病理性膝关节结构的稳健和准确分割。所采用的形状模型和神经网络分别使用来自骨关节炎（OAI）和MICCAI大挑战“膝关节图像分割2010”（SKI10）的数据进行训练。我们在SKI10挑战的40个验证和50个提交数据集上评估了我们的方法。在这项挑战中，首次实现了与人类读者的观察者间差异相当的准确性。此外，所提出的方法的质量进行了彻底的评估，使用各种措施的数据从OAI，即507手动分割的骨和软骨，和88个额外的手动分割软骨。我们的方法为两个OAI数据集产生子体素精度。我们使507手动分割，以及我们的实验设置公开，以进一步帮助医学图像分割领域的研究。总之，将通过CNN的局部分类与通过SSM的统计解剖知识相结合，可以从MRI数据中获得最先进的膝盖骨和软骨分割方法。(C)2018爱思唯尔B.V.保留所有权利。
We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data from the Osteoarthritis (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets from the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers is achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We make the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data. (C) 2018 Elsevier B.V. All rights reserved.