Spinal Osteophyte Detection via Robust Patch Extraction on minimally
  annotated X-rays
Soumya Snigdha Kundu Yuanhan Mo Nicharee Srikijkasemwat Bartłomiej W. Papiez
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, SegPatch 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.