Osteoporosis is a public health problem characterized by increased fracture
risk secondary to low bone mass and microarchitectural deterioration of bone
tissue. Almost all fractures of the hip require hospitalization and major
surgery. Early diagnosis of osteoporosis plays an important role in preventing
osteoporotic fracture. Magnetic resonance imaging (MRI) has been successfully
performed to image trabecular bone architecture in vivo proving itself as the
practical imaging modality for bone quality assessment. However, segmentation
of the whole proximal femur is required to measure bone quality and assess
fracture risk precisely. Manual segmentation of the proximal femur is
time-intensive, limiting the use of MRI measurements in the clinical practice.
To overcome this bottleneck, robust automatic proximal femur segmentation
method is required. In this paper, we propose to use deep convolutional neural
networks (CNNs) for an automatic proximal femur segmentation using structural
MR images. We constructed a dataset with 62 volumetric MR scans that are
manually-segmented for proximal femur. We performed experiments using two
different CNN architectures and achieved a high dice similarity score of 0.95.