Deng Yu, Wang Ling, Zhao Chen, Tang Shaojie, Cheng Xiaoguang, Deng Hong-Wen, Zhou Weihua
School of Automation, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China.
Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China.
Med Biol Eng Comput. 2022 May;60(5):1417-1429. doi: 10.1007/s11517-022-02529-9. Epub 2022 Mar 24.
Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip fracture risk. In this study, we proposed an approach based on deep learning for the fast and automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments. A three-dimensional (3D) end-to-end fully convolutional neural network (CNN), which can better combine the information among neighbor slices and get more accurate segmentation results by 3D CNN, was developed for our segmentation task. The separation of cortical and trabecular bones derived from the QCT software MIAF-Femur was used as the segmentation reference. Two models with the same network structures were trained, and they achieved a dice similarity coefficient (DSC) of 97.82% and 96.53% for the periosteal and endosteal contours, respectively. Compared with MIAF-Femur, it takes half an hour to segment a case, and our CNN model takes a few minutes. To verify the excellent performance of our model for proximal femoral segmentation, we measured the volumes of different parts of the proximal femur and compared it with the ground truth, and the relative errors of femur volume between predicted result and ground truth are all less than 5%. This approach will be expected helpful to measure the bone mineral densities of cortical and trabecular bones, and to evaluate the bone strength based on FEA.
近端股骨的CT自动分割在骨科疾病中具有巨大的应用潜力，尤其是在基于成像的髋部骨折风险评估方面。在本研究中，我们提出了一种基于深度学习的方法，用于快速自动提取近端股骨的骨膜和骨内膜轮廓，以区分皮质骨和小梁骨区域。我们开发了一种三维（3D）端到端全卷积神经网络（CNN）用于分割任务，该网络能更好地整合相邻切片间的信息，并通过3D CNN获得更准确的分割结果。将源自QCT软件MIAF-Femur的皮质骨和小梁骨分离用作分割参考。训练了两个具有相同网络结构的模型，它们对骨膜和骨内膜轮廓的骰子相似系数（DSC）分别达到了97.82%和96.53%。与MIAF-Femur相比，分割一个病例MIAF-Femur需要半小时，而我们的CNN模型只需几分钟。为验证我们的模型在近端股骨分割方面的优异性能，我们测量了近端股骨不同部位的体积并与真实值进行比较，预测结果与真实值之间股骨体积的相对误差均小于5%。该方法有望有助于测量皮质骨和小梁骨的骨密度，并基于有限元分析评估骨强度。
