Spinal osteophyte detection via robust patch extraction on minimally annotated x-rays

Conference item

The development and progression of arthritis is strongly associated with osteophytes, which are small and elusive bone growths. This paper presents one of the first efforts towards automated spinal osteophyte detection in spinal X-rays. A novel automated patch extraction process, called SegPatch, has been proposed based on deep learning-driven vertebrae segmentation and the enlargement of mask contours. A final patch classification accuracy of 84.5% is secured, surpassing a baseline tiling-based patch generation technique by 9.5%. This demonstrates that even with limited annotations, Seg-Patch can deliver superior performance for detection of tiny structures such as osteophytes. The proposed approach has potential to assist clinicians in expediting the process of manually identifying osteophytes in spinal X-ray.

Authors: Kundu, SS; Mo, Y; Srikijkasemwat, N; Papiez, BW

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2024 IEEE International Symposium on Biomedical Imaging (ISBI)
• Pages: 1-5
• Publication date: 2024-08-22
• Event title: 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)
• Event location: Athens, Greece
• Event website: https://biomedicalimaging.org/2024/
• Event start date: 2024-05-27
• Event end date: 2024-05-30
• DOI: 10.1109/ISBI56570.2024.10635802
• EISSN: 1945-8452
• ISSN: 1945-7928
• EISBN: 9798350313338
• ISBN: 9798350313345
• Language: English
• Keywords: training; accuracy; annotations; magnetic resonance imaging; x-rays; bones; arthritis