Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network

• Published in: Multimedia tools and applications 2018-05, Vol.77 (9), p.10921-10937
• Main Authors: Murakami, Seiichi, Hatano, Kazuhiro, Tan, JooKooi, Kim, Hyoungseop, Aoki, Takatoshi
• Format: Article
• Language: English
• Subjects: Arthritis; Artificial neural networks; Computer Communication Networks; Computer Science; Damage assessment; Data Structures and Information Theory; Image segmentation; Multimedia Information Systems; Multiscale analysis; Neural networks; Radiographs; Rheumatoid arthritis; Snakes; Special Purpose and Application-Based Systems
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-017-5449-4

Although radiographic assessment of joint damage is essential in characterizing disease progression and prognosis in patients with rheumatoid arthritis (RA), it is often difficult even for trained radiologists to find radiographic changes on hand and foot radiographs because lesion changes are often subtle. This paper proposes a novel quantitative method for automatically detecting bone erosion on hand radiographs to assist radiologists. First, the proposed method performs with the crude segmentation of phalanges regions from hand radiograph and extracts the detailed phalanges regions by the multiscale gradient vector flow (MSGVF) Snakes method. Subsequently, the region of interest (ROI; 40 × 40 pixels) is automatically set on the contour line of the segmented phalanges by the MSVGF algorithm. Finally, these selected ROIs are identified by the presence or absence of bone erosion using a deep convolutional neural network classifier. This proposed method is applied to the hand radiographs of 30 cases with RA. The true-positive rate and the false-positive rate of the proposed method are 80.5% and 0.84%, respectively. The number of false-positive ROIs is 3.3 per case. We believe that the proposed method is useful for supporting radiologists in imaging diagnosis of RA.