Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks
This work addresses the clinical need for faster bone quality measurements in osteoporosis management, though it is incremental as it applies existing CNN methods to a specific medical imaging task.
The paper tackled the problem of time-consuming manual segmentation of proximal femur MR images for bone quality assessment by developing an automatic method using deep convolutional neural networks, achieving a high dice similarity score of 0.94±0.05 with precision of 0.95±0.02 and recall of 0.94±0.08.
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.