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.