nnUnet-based automated quantification of wrist joint synovitis volume in RA patients: a feasibility study

Background Synovitis is the key inflammatory feature of rheumatoid arthritis (RA). Quantitative assessment of synovitis better correlates with patient outcomes than semi-quantitative assessment but it is time-consuming. Purpose To develop and validate an automated model for segmentation and quantification of wrist synovial tissue volume on post-contrast fat-suppressed T1-weighted MRI. Material and Methods Early rheumatoid arthritis patients (symptoms for ≤24 months) at a single center were recruited at baseline and were followed-up at year-1 and year-8. Post-contrast axial fat-suppressed T1-weighted images of the most symptomatic wrist were acquired at 3.0 T. One observer manually segmented consecutive synovitis areas on all MRI datasets. A framework, based on the convolutional neural network, nnU-Net, was trained and validated (5-fold cross-validation with image level splits) with 295 image datasets used for model training and validation. Rheumatoid arthritis MRI score (RAMRIS) was used to semi-quantitatively grade synovitis. Manually segmented synovial volume by a single reader was used as the reference standard. Forty-five external image datasets from two different imaging centers were used to test generalizable applicability. Results For automated synovitis segmentation, the overall Sørensen-Dice similarity coefficient (DSC) was 0.75 ± 0.11(mean ± sd) compared to manual segmentation. Higher DSC values were found in patients with moderate (0.80 ± 0.06) and severe (0.84 ± 0.05) degrees of synovitis. The model had a similar performance with external-acquired data (DSC value : 0.70 ± 0.20). Predicted and manually segmented synovitis volume measurements showed excellent agreement (Pearson correlation: r = 0.975, p < 0.001). Conclusion A fully automated model quantified wrist synovial tissue volume with good agreement to manual reference and maintained performance on external data, supporting potential use in clinical studies and prospective evaluation in practice.