Related Experiment Video
Updated: Apr 5, 2026
Automated Joint Space Detection Improves Bone Segmentation Accuracy
Published on: November 28, 2025
Fast automated segmentation of wrist bones in magnetic resonance images
Justyna Włodarczyk1, Wadim Wojciechowski2, Kamila Czaplicka1
1Jagiellonian University, prof. Stanisława Łojasiewicza 11, 30-348 Krakow, Poland.
Purpose:
According to current recommendations in diagnostics of rheumatoid arthritis (RA), Magnetic resonance (MR) images of wrist joints are used to evaluate three main signs of RA: synovitis, bone edema and bone erosions. In this paper we present an efficient method for segmentation of 15 bones present on MR images of the wrist which is inevitable for future computer-assisted diagnosis system for RA lesions.
Method:
The segmentation procedure consists of two stages. The first stage is evaluation of markers (parts of bones working as seeds for the watershed algorithm) for bones in every joint: the distal parts of ulna and radius, the proximal parts of metacarpal bones and carpal bones. In the second stage the watershed from markers algorithm is applied based on the markers determined in the previous stage and the wrist bones are segmented. The markers were found using Multi Otsu algorithm along with custom method for filtering bones from other tissues.
Results:
We analyzed 34 MR images. The automated segmentations were compared with manual segmentations using metrics: accuracy ACC derived from area under ROC curve AUC, Dice coefficient and mean absolute distance MAD. The mean (standard deviation) values of ACC, Dice and MAD were 0.99 (0.02), 0.98 (0.04) and 1.21 (0.39), respectively.
Conclusion:
The results of this study prove that our method is efficient and gives satisfactory results for segmentation of bones on low-field MR images of the wrist.
