A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images. (February 2022)

• Record Type: Journal Article
• Authors: Fiorentino, Maria Chiara; Cipolletta, Edoardo; Filippucci, Emilio; Grassi, Walter; Frontoni, Emanuele; Moccia, Sara
• Is Part Of: Computers in biology and medicine. Volume 141(2022)
• Publication Date: 2022-02
• Subjects: Ultrasound -- Rheumatoid arthritis -- Hyaline cartilage -- Distance fields -- Convolutional neural network; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105117
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint targets of this disease. Cartilage damage is one of the most relevant determinants of physical disability in RA patients. Cartilage damage is nowadays assessed by clinicians, which manually measure cartilage thickness in ultrasound (US) imaging. This poses issues relevant to intra-and inter-observer variability. Relying on the acquisition of metacarpal-head US images from 38 subjects, this work addresses the problem of automatic cartilage-thickness measurement by designing a new deep-learning (DL) framework. Methods: The framework consists of a Convolutional Neural Network (CNN), responsible for regressing cartilage-interface distance fields, followed by a post-processing step to delineate the two cartilage interfaces from the predicted distance fields and compute the cartilage thickness. Results: Our framework achieved encouraging results with a mean absolute difference ( ADF ) of 0.032 (±0.026) mm against manual thickness annotation by an expert clinician. When evaluating the intra-observer variability, we obtained an ADF = 0.036 (±0.028) mm. Conclusion: The proposed framework achieved an ADF against manual annotation that was comparable to the intra-observer variability, proving the potential of DL in the field. Significance: This work is the first to address the problem of automatic cartilage-thickness estimation in US rheumatological images with DL, paving the way for future research in the field. From a clinical perspective, the proposed framework proved to be a valuable tool to support the clinical routine increasing the reproducibility of cartilage thickness measurements. Graphical abstract: Image 1 Highlights: This paper addresses the problem of automatic cartilage-thickness estimation in US rheumatological images. Cartilage-thickness estimation is tackled by regressing cartilage interface position by means of distance field. The dataset (38 subjects) is acquired in the actual clinical practice and annotated by an expert clinician. The framework achieved a mean absolute difference of 0.032 mm that was comparable to the intra-observer variability.