aceDeep Learning-Based Scapular Morphology Assessment Pipeline for Glenoid Segmentation and Landmark Localization
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 cl…
On retrospective shoulder CT data, the pipeline segmented the glenoid with high overlap and placed five scapular landmarks within 1.0 to 2.0 mm on average. Each case took about 22.55 seconds, versus slow manual annotation.
Evidence
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-10-09
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Truvace Impact Record TRV-2026-1634, v1: “Deep Learning-Based Scapular Morphology Assessment Pipeline for Glenoid Segmentation and Landmark Localization.” Truvace, 2026-10-17. /record/TRV-2026-1634 (accessed at citation time). sha256 3938793e8d8bb434…
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