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A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images

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Document pages: 12 pages

Abstract: Purpose: Proximal femur image analyses based on quantitative computedtomography (QCT) provide a method to quantify the bone density and evaluateosteoporosis and risk of fracture. We aim to develop a deep-learning-basedmethod for automatic proximal femur segmentation. Methods and Materials: Wedeveloped a 3D image segmentation method based on V-Net, an end-to-end fullyconvolutional neural network (CNN), to extract the proximal femur QCT imagesautomatically. The proposed V-net methodology adopts a compound loss function,which includes a Dice loss and a L2 regularizer. We performed experiments toevaluate the effectiveness of the proposed segmentation method. In theexperiments, a QCT dataset which included 397 QCT subjects was used. For theQCT image of each subject, the ground truth for the proximal femur wasdelineated by a well-trained scientist. During the experiments for the entirecohort then for male and female subjects separately, 90 of the subjects wereused in 10-fold cross-validation for training and internal validation, and toselect the optimal parameters of the proposed models; the rest of the subjectswere used to evaluate the performance of models. Results: Visual comparisondemonstrated high agreement between the model prediction and ground truthcontours of the proximal femur portion of the QCT images. In the entire cohort,the proposed model achieved a Dice score of 0.9815, a sensitivity of 0.9852 anda specificity of 0.9992. In addition, an R2 score of 0.9956 (p<0.001) wasobtained when comparing the volumes measured by our model prediction with theground truth. Conclusion: This method shows a great promise for clinicalapplication to QCT and QCT-based finite element analysis of the proximal femurfor evaluating osteoporosis and hip fracture risk.

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