Can We Achieve Accurate Automatic Shoulder Bone Segmentation With a Limited Number of CT Scans?

Abstract

Abstract The scarcity of medical imaging data poses a significant challenge for the development of automated segmentation models. This study addresses this limitation by focusing on 3D reconstruction of shoulder bones. We evaluated manual and automatic segmentation methods: DL-based using MONAILabel and commercial software (Materialise Mimics), using only 14 CT scans manually labeled for the humerus, scapula, and clavicle. To mitigate data limitations, we applied data augmentation techniques and trained convolutional neural networks optimized for medical imaging, systematically tuning hyperparameters to assess their impact. Segmentation performance was measured using Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). Our DL model achieved DSC scores of 0.96 (humerus), 0.90 (scapula), and 0.82 (clavicle). Fractures were preserved, enabling accurate 3D reconstruction and measurement. The commercial software achieved similar accuracy. This study highlights the feasibility of using DL for accurate shoulder segmentation in data-constrained scenarios and supports the use of open-source tools as viable alternatives in clinical workflows. Future work will focus on expanding the dataset and further refining the CNN models.

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