FingerNet: Deep learning-based robust finger joint detection from radiographs

Radiographic image assessment is the most common method used to measure physical maturity and diagnose growth disorders, hereditary diseases and rheumatoid arthritis, with hand radiography being one of the most frequently used techniques due to its simplicity and minimal exposure to radiation. Finger joints are considered as especially important factors in hand skeleton examination. Although several automation methods for finger joint detection have been proposed, low accuracy and reliability are hindering full-scale adoption into clinical fields. In this paper, we propose FingerNet, a novel approach for the detection of all finger joints from hand radiograph images based on convolutional neural networks, which requires little user intervention. The system achieved 98.02% average detection accuracy for 130 test data sets containing over 1,950 joints. Further analysis was performed to verify the system robustness against factors such as epiphysis and metaphysis in different age groups.