We would like to make the following corrections to our article "Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MR": Abstract, the sentence "One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5 min after contrast injection" should be "One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 100 s. after contrast injection".

Rheumatoid arthritis (RA) frequently affects the joints of the hands, with joint space narrowing (JSN) representing an important early marker of structural damage. The semi-quantitative Sharp/van der Heijde (SvdH) scoring system is widely used in clinical practice but is inherently subjective and susceptible to observer variability. Moreover, the complex anatomy of the wrist and substantial overlap of carpal bones pose challenges for automated quantitative assessment of wrist JSN on routine radiographs. This study aimed to introduce a novel quantitative assessment perspective and to clinically validate an automated, compactness-related quantification framework for evaluating wrist JSN in RA. This study initially enrolled 51 patients with RA. After excluding one case with severe carpal fusion that precluded anatomical differentiation, 50 patients (44 females and 6 males) were included in the final analysis. The cohort had a mean age of 61 years (range: 21–82), a median symptom duration of 9 years (IQR: 1–32), and a median follow-up interval for bilateral hand radiographs of 1.06 years (IQR: 0.82–1.30). To quantify global wrist JSN, 10 compactness-related metrics were computed based on the spatial distribution of bone centroids extracted from carpal segmentation masks. These metrics were validated against the wrist JSN subscore of the SvdH score (SvdH-JSN_wrist) and the total Sharp score (TSS) as gold standards. Several distance-based metrics among the compactness-related metrics showed significant negative correlations with the wrist joint space narrowing subscore of the Sharp/van der Heijde score (SvdH-JSN_wrist). Specifically, mean-pairwise-distance ($MPD$), root-mean-square-radius ($RMSR$), and median-radius ($R50$) showed moderate to strong correlations (r = −0.52 to −0.63, all $p\le 0.0001$) that were consistent at BL and FU. Correlations with TSS were weaker overall, with only $R50$ and its normalized form showing stable negative correlations (r = −0.40 to −0.43, p < 0.01). Longitudinal analyses showed limited correlations between metric changes and clinical score changes. The proposed automated compactness quantification framework enables objective and reliable assessment of wrist JSN on standard radiographs and complements conventional scoring systems by supporting automated and standardized evaluation of RA-related wrist structural changes.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that results in progressive articular destruction and severe disability. Joint space narrowing (JSN) progression has been regarded as an important indicator for RA progression and has received sustained attention. In the diagnosis and monitoring of RA, radiology plays a crucial role to monitor joint space. A new framework for monitoring joint space by quantifying JSN progression through image registration in radiographic images has been developed. This framework offers the advantage of high accuracy, however, challenges do exist in reducing mismatches and improving reliability. In this work, a deep intra-subject rigid registration network is proposed to automatically quantify JSN progression in the early stage of RA. In our experiments, the mean-square error of Euclidean distance between moving and fixed image is 0.0031, standard deviation is 0.0661 mm, and the mismatching rate is 0.48\%. The proposed method has sub-pixel level accuracy, exceeding manual measurements by far, and is equipped with immune to noise, rotation, and scaling of joints. Moreover, this work provides loss visualization, which can aid radiologists and rheumatologists in assessing quantification reliability, with important implications for possible future clinical applications. As a result, we are optimistic that this proposed work will make a significant contribution to the automatic quantification of JSN progression in RA.