WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI. (January 2018)

• Record Type: Journal Article
• Authors: Foster, Brent; Joshi, Anand A.; Borgese, Marissa; Abdelhafez, Yasser; Boutin, Robert D.; Chaudhari, Abhijit J.
• Is Part Of: Computerized medical imaging and graphics. Volume 63(2018)
• Page Start: 31
• Page End: 40
• Publication Date: 2018-01
• Subjects: Bone segmentation -- Carpal bones -- MRI segmentation; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2017.12.003
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: A computational toolkit for fast carpal bone segmentation from MRI was created. Method incorporated anatomical knowledge and shape-detection level sets. Achieved shorter computational time and higher agreement with 2 expert observers. Abstract: Segmentation of the carpal bones from 3D imaging modalities, such as magnetic resonance imaging (MRI), is commonly performed for in vivo analysis of wrist morphology, kinematics, and biomechanics. This crucial task is typically carried out manually and is labor intensive, time consuming, subject to high inter- and intra-observer variability, and may result in topologically incorrect surfaces. We present a method, WRist Image Segmentation Toolkit (WRIST), for 3D semi-automated, rapid segmentation of the carpal bones of the wrist from MRI. In our method, the boundary of the bones were iteratively found using prior known anatomical constraints and a shape-detection level set. The parameters of the method were optimized using a training dataset of 48 manually segmented carpal bones and evaluated on 112 carpal bones which included both healthy participants without known wrist conditions and participants with thumb basilar osteoarthritis (OA). Manual segmentation by two expert human observers was considered as a reference. On the healthy subject dataset we obtained a Dice overlap of 93.0 ± 3.8, Jaccard Index of 87.3 ± 6.2, and a Hausdorff distance of 2.7 ± 3.4 mm, while on the OA dataset we obtained a Dice overlap of 90.7 ± 8.6, Jaccard Index of 83.0 ± 10.6, and a Hausdorff distance of 4.0 ± 4.4 mm. The short computational time of 20.8 s per bone (or 5.1 s per bone in the parallelized version) and the high agreement with the expert observers gives WRIST the potential to be utilized in musculoskeletal research.