Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA.
Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, 10016, USA.
Sci Rep. 2018 Nov 7;8(1):16485. doi: 10.1038/s41598-018-34817-6.
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.
磁共振成像（MRI）已被提议作为一种补充方法来测量骨质量和评估骨折风险。然而，骨骼 MRI 图像的手动分割非常耗时，限制了 MRI 测量在临床实践中的应用。本文旨在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。对 86 名受试者的股骨近端容积结构 MRI 图像数据集进行了手动分割。我们通过训练两种不同的 CNN 架构，使用多个初始特征图、层和扩张率进行实验，并使用四折交叉验证将其分割性能与手动分割的金标准进行比较。使用 CNN 进行股骨近端的自动分割达到了 0.95±0.02 的高 Dice 相似性评分，精度=0.95±0.02，召回率=0.95±0.03。CNN 提供的高精度分割有可能有助于将骨骼结构 MRI 测量引入骨质疏松症管理的临床实践。
