A CNN-based strategy to automate contour detection of the hip and proximal femur using DXA hip images from longitudinal databases (CLSA and CaMos)

Hip fractures contribute significantly to mortality in older adults. New methods to identify those at risk use dual-energy X-ray absorptiometry (DXA) images and advanced image processing. However, DXA images have an overlapping femur and pelvis and may contain boundary lines, making automation challenging. Herein, a 5-layer U-net convolutional neural network (CNN) was developed to segment the femur from hip DXA images. Images were used from the …