  Osteoporosis is a common bone disease that occurs when the creation of new
bone does not keep up with the loss of old bone, resulting in increased
fracture risk. Adults over the age of 50 are especially at risk and see their
quality of life diminished because of limited mobility, which can lead to
isolation and depression. We are developing a robust screening method capable
of identifying individuals predisposed to hip fracture to address this clinical
challenge. The method uses finite element analysis and relies on segmented
computed tomography (CT) images of the hip. Presently, the segmentation of the
proximal femur requires manual input, which is a tedious task, prone to human
error, and severely limits the practicality of the method in a clinical
context. Here we present a novel approach for segmenting the proximal femur
that uses a deep convolutional neural network to produce accurate, automated,
robust, and fast segmentations of the femur from CT scans. The network
architecture is based on the renowned u-net, which consists of a downsampling
path to extract increasingly complex features of the input patch and an
upsampling path to convert the acquired low resolution image into a high
resolution one. Skipped connections allow us to recover critical spatial
information lost during downsampling. The model was trained on 30 manually
segmented CT images and was evaluated on 200 ground truth manual segmentations.
Our method delivers a mean Dice similarity coefficient (DSC) and 95th
percentile Hausdorff distance (HD95) of 0.990 and 0.981 mm, respectively.
