Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.
Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.
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DOI：
10.1038/s41598-018-34817-6
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发表时间：
2018-11-07
影响因子：
4.6
通讯作者：
Chang G
中科院分区：
文献类型：
--
作者：
Deniz CM;Xiang S;Hallyburton RS;Welbeck A;Babb JS;Honig S;Cho K;Chang G
磁共振成像（MRI）已被提议作为测量骨质和评估骨折风险的补充方法。然而，人工分割的MR图像的骨是耗时的，限制了MRI测量在临床实践中的使用。本文的目的是提出一种基于深度卷积神经网络（CNN）的自动股骨近端分割方法。本研究获得了机构审查委员会的批准，并获得了所有受试者的书面知情同意书。由专家手动分割来自86名受试者的股骨近端体积结构MR图像数据集。我们通过训练两种不同的CNN架构来进行实验，这些CNN架构具有多个初始特征图，层和膨胀率，并使用四重交叉验证来测试其分割性能。使用CNN对股骨近端进行自动分割，获得了0.95 ± 0.02的高骰子相似性分数，精确度= 0.95 ± 0.02，召回率= 0.95 ± 0.03。CNN提供的高分割精度有可能帮助将骨质量的结构MRI测量用于骨质疏松症的临床实践。
Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.
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