  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 subject were
manually-segmented by an expert. We performed experiments by training two
different CNN architectures with multiple number of initial feature maps and
layers, and tested their segmentation performance against the gold standard of
manual segmentations using four-fold cross-validation. Automatic segmentation
of the proximal femur achieved a high dice similarity score of 0.94$\pm$0.05
with precision = 0.95$\pm$0.02, and recall = 0.94$\pm$0.08 using a CNN
architecture based on 3D convolution exceeding the performance of 2D CNNs. 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.
