Translator Disclaimer
13 March 2019 Automatic detection and localization of bone erosion in hand HR-pQCT
Author Affiliations +
Rheumatoid arthritis (RA) is an inflammatory disease which afflicts the joints with arthritis and periarticular bone destruction as a result. One of its central features is bone erosion, a consequence of excessive bone resorption and insufficient bone formation. High-resolution peripheral quantitative computed tomography (HR-pQCT) is a promising tool for monitoring RA. Quantification of bone erosions and detection of possible progression is essential in the management of treatment. Detection is performed manually and is a very demanding task as rheumatologists must annotate hundreds of 2D images and inspect any region of the bone structure that is suspected to be a sign of RA. We propose a 2D based method which combines an accurate segmentation of bone surface boundary and classification of patches along the surface as healthy or eroded. We use a series of classical image processing methods to segment CT volumes semi-automatically. They are used as training data for a U-Net. We train a Siamese net to learn the difference between healthy and eroded patches. The Siamese net alleviates the problem of highly imbalanced class labels by providing a base for one-shot learning of differences between patches. We trained and tested the method using 3 full HR-pQCT scans with bone erosion of various size. The proposed pipeline succeeded in classifying healthy and eroded patches with high precision and recall. The proposed algorithm is a preliminary work to demonstrate the potential of our pipeline in automating the process of detecting and locating the eroded regions of bone surfaces affected by RA.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jintao Ren, Arash Moaddel H., Ellen M. Hauge, Kresten K. Keller, Rasmus K. Jensen, and François Lauze "Automatic detection and localization of bone erosion in hand HR-pQCT", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095022 (13 March 2019);

Back to Top