Introduction
Fractures in the forearm are common and sometimes result in limitations of pronation/supination. Besides malunion as a possible cause, soft tissue involvement may play a more significant role as well. More insight in both causes of impaired forearm rotation could help to treat patients in the least invasive way as possible, potentially avoiding invasive corrective osteotomies in some patients.

Objectives
This study aimed to provide a deep-learning based framework for automated segmentation of anatomical structures involved in pronation/supination of the forearm on magnetic resonance (MR) images. This approach allows for visualization and quantitative analysis of the patient-specific anatomy, enabling efficient identification of soft tissue structures that may contribute to impaired forearm rotation.

Methods and materials
Manual ground truth annotations of six anatomical structures (radius, ulna, interosseous membrane, m. pronator quadratus, m. pronator teres, m. supinator) were performed on 24 fast-recovery fast spin-echo T2-weighted (FRFSE T2) in-phase Dixon images of the forearm. The dataset contained an equal distribution between affected and unaffected, and left and right forearms. Two nnU-Net configurations (2D and 3D) were trained on 20 manually segmented forearms using 5-fold cross-validation for segmentation of the six structures. An ensemble was created by combining predictions from both fully-trained models. A hold-out test set of 4 forearms was used to evaluate segmentation performance using the Dice similarity coefficient (DSC) and the average symmetric surface distance (ASSD) metrics. Additionally, relative volume difference (Δrel) between ground truth and predicted segmentations were computed to assess under- or oversegmentation.

Results
The 3D model achieved the best segmentation performance, with a median DSC score of 0.894 (IQR=0.094) and a median ASSD of 0.324 (IQR=0.386) mm. It slightly undersegmented the anatomy, with a median relative volume difference of -2.7% (IQR=7.1%). Qualitative results revealed that the 3D model produced segmentation masks that contained fewer and less severe segmentation errors compared to the 2D model and ensemble. Minor segmentation errors were observed in the interosseous membrane, the proximal part of the m. pronator quadratus and the insertion of the m. pronator teres in some cases.

Conclusion
The 3D nnU-Net model has proven its suitability for clinical use, enabling fast, reproducible and precise segmentation of structures involved in pronation/supination of the forearm. This approach facilitates bilateral comparisons of soft tissue structures through visual assessment and quantitative analysis, supporting patient-specific and minimally invasive decision-making.