  Automatic CT segmentation of proximal femur is crucial for the diagnosis and
risk stratification of orthopedic diseases; however, current methods for the
femur CT segmentation mainly rely on manual interactive segmentation, which is
time-consuming and has limitations in both accuracy and reproducibility. In
this study, we proposed an approach based on deep learning for the 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, which can better combine
the information between neighbor slices and get more accurate segmentation
results, was developed for our segmentation task. 100 subjects aged from 50 to
87 years with 24,399 slices of proximal femur CT images were enrolled in this
study. The separation of cortical and trabecular bone derived from the QCT
software MIAF-Femur was used as the segmentation reference. We randomly divided
the whole dataset into a training set with 85 subjects for 10-fold
cross-validation and a test set with 15 subjects for evaluating the performance
of models. Two models with the same network structures were trained and they
achieved a dice similarity coefficient (DSC) of 97.87% and 96.49% for the
periosteal and endosteal contours, respectively. To verify the excellent
performance of our model for femoral segmentation, we measured the volume of
different parts of the femur and compared it with the ground truth and the
relative errors between predicted result and ground truth are all less than 5%.
It demonstrated a strong potential for clinical use, including the hip fracture
risk prediction and finite element analysis.
