Deep Learning-Based Scapular Morphology Assessment Pipeline for Glenoid Segmentation and Landmark Localization.
Authors
Affiliations (3)
Affiliations (3)
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China.
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China. [email protected].
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, China. [email protected].
Abstract
Scapular morphology plays a critical role in the diagnosis and treatment of shoulder disorders. However, current assessment methods primarily rely on manual annotation of three-dimensional computed tomography (3D CT) scans by clinicians, which are time-consuming, labor-intensive, and prone to inter-observer variability. The objective of this study is to develop and validate a deep learning-based open-source pipeline for automated scapular morphology assessment, aiming to improve accuracy, reproducibility, and clinical efficiency. Descriptive Laboratory Study; Level of evidence, 2. We retrospectively collected 793 CT images of the shoulder from 774 patients (mean age, 60.00 ± 18.62 years). A 2D U-Net model performed high-resolution glenoid segmentation (native-resolution axial CT slices, no down-sampling), and a 3D nnUNet model was employed to localize five anatomical scapular landmarks: trigonum spinae (TS), angulus inferior (AI), processus coracoideus (PC), acromion (AC), and angulus acromialis (AA). Model performance was evaluated using the Dice coefficient and precision-recall metrics for glenoid segmentation and the Euclidean distance between the predicted and ground truth landmark position. This pipeline was designed as an automated open-source framework that can be readily integrated into clinical workflows or research environments through clinician-preferred user interface. The glenoid segmentation model achieved a 3D Dice coefficient of 98.46%, with a precision of 96.59% and a recall of 97.30%. The mean Euclidean localization error for the five scapular landmarks ranged from 1.0 to 2.0 mm. The entire assessment process took average 22.55 s per case. The proposed deep learning pipeline enables accurate, efficient, and reproducible assessment of scapular morphology. The proposed approach facilitates reliable and efficient diagnosis, offering a promising tool for clinical and research applications.