Comparison of Foundation Models MedSAM and DINOv3 with the nnU-Net Framework for Bone Metastasis Segmentation in Computed Tomography Scans

This study compares three methods for 2D bone metastasis segmentation on computed tomography slices-the self-configuring nnU-Net pipeline, a fine-tuned DINOv3 foundation model, and a prompt-free MedSAM foundation model adaptation-to assess their suitability for clinical-grade lesion delineation. Methods: A dataset of 2D CT slices from 88 patients (11,006 image–label pairs) was annotated by experts. The three models were trained and evaluated under comparable conditions, using model-specific native input resolutions and training schedules. Performance was evaluated using the Dice similarity coefficient (DSC) and Normalized Hausdorff distance (NHD) on a held-out test set, with a separate cohort of previously unseen patients. On a held-out test set, the MedSAM, DINOv3, and nnU-Net models achieved the following Dice scores: 0.6280, 0.4480, and 0.6849, respectively. Additionally, on a held-out test set, the MedSAM, DINOv3, and nnU-Net models achieved the following normalized Hausdorff distances: 0.1008, 0.1019, and 0.0473, respectively. In conclusion, the nnU-Net framework provides robust segmentation performance and serves as a strong baseline for 2D slice-wise bone metastasis delineation even with limited annotated data.