Deep Learning-Based Automated Segmentation of the Suprascapular Nerve and Adjacent Structures in Dynamic Supraclavicular Ultrasound.

OBJECTIVE

The suprascapular nerve (SSN) provides major motor and sensory innervation to the shoulder. Its accurate identification on ultrasound is challenging because of its small size, low contrast and proximity to structures with similar echotexture. This study aimed to develop and evaluate a deep learning-based approach for multi-class segmentation of the SSN and adjacent structures in dynamic supraclavicular ultrasound.

METHODS

Dynamic ultrasound videos (n = 80) from 42 healthy adults were manually annotated for the SSN, brachial plexus, subclavian artery and omohyoid muscle. A Double U-Net architecture was implemented, incorporating a VGG-19 pre-trained encoder in the first stage and a randomly initialized encoder in the second stage, together with Atrous Spatial Pyramid Pooling and Squeeze-and-Excitation blocks. Ablation experiments examined the effects of encoder choice, architectural design, annotation strategy, loss function and output weighting. Segmentation performance was evaluated using Dice similarity coefficients on an independent test set.

RESULTS

The Double U-Net significantly outperformed the baseline U-Net across all annotated structures. For SSN segmentation, the mean Dice coefficient improved from 0.51 ± 0.19 to 0.68 ± 0.11 (p = 0.03), with greater stability across test videos. A pre-trained VGG-19 encoder showed higher Dice scores than alternative encoders, without statistical significance (p = 0.12). Ablation analyses confirmed complementary contributions of both stages (p = 0.52). Multi-structure supervision yielded modest benefit, with optimal performance when all four structures were annotated (Dice 0.68 ± 0.11). Focal loss achieved the highest average Dice score, and optimal performance was obtained by emphasizing the final output with limited intermediate supervision (α:β = 0.1:0.9).

CONCLUSION

The proposed Double U-Net enables reliable multi-class segmentation of the SSN and surrounding structures in dynamic supraclavicular ultrasound, supporting its potential clinical application in suprascapular neuropathy.