Automatic Segmentation of Hip Osteophytes in DXA Scans Using U-Nets

Abstract

Osteophytes are distinctive radiographic features of osteo-arthritis (OA) in the form of small bone spurs protruding from joints that contribute significantly to symptoms. Identifying the genetic determinants of osteophytes would improve the understanding of their biological pathways and contributions to OA. To date, this has not been possible due to the costs and challenges associated with manually outlining osteophytes in sufficiently large datasets. Automatic systems that can segment osteophytes would pave the way for this research and also have potential clinical applications. We propose, to the best of our knowledge, the first work on automating pixel-wise segmentation of osteophytes in hip dual-energy x-ray absorptiometry scans (DXAs). Based on U-Nets, we developed an automatic system to detect and segment osteophytes at the superior and the inferior femoral head, and the lateral acetabulum. The system achieved sensitivity, specificity, and average Dice scores (±std) of (0.98, 0.92, 0.71 ± 0.19 ) for the superior femoral head [793 DXAs], (0.96, 0.85, 0.66 ± 0.24 ) for the inferior femoral head [409 DXAs], and (0.94, 0.73, 0.64 ± 0.24 ) for the lateral acetabulum [760 DXAs]. This work enables large-scale genetic analyses of the role of osteophytes in OA, and opens doors to using low-radiation DXAs for screening for radiographic hip OA.