ArticleNeurospine2026
Domain-Adapted Foundation Models for Single-Click Surgical Instrument Segmentation in Spinal Endoscopy.
Article in Neurospine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveAccurate segmentation of surgical instruments in endoscopic images is essential for developing computer-assisted surgical systems, yet creating annotated training datasets remains labor-intensive. This study aimed to evaluate the efficiency of point-based interactive segmentation using foundation models for surgical instrument annotation in spinal endoscopy.
methodsWe conducted a retrospective study comparing point-based segmentation performance between Segment Anything Model (SAM) 2.0, SAM 2.1, and domain-adapted MedSAM 2.1 for surgical instrument segmentation in spinal endoscopy. A test set of 308 images from 10 patients undergoing full-endoscopic lumbar decompression/discectomy or biportal endoscopic decompression/discectomy was evaluated. Models were assessed using standardized point prompts placed at the ground truth mask centroid. Primary outcomes included Dice similarity coefficient at single-click and success rate (Dice≥0.85). Secondary analysis evaluated click-to-quality metrics across iterative refinement up to 10 clicks.
resultsAt single-click, MedSAM 2.1 achieved significantly higher Dice scores than SAM 2.1 (0.937±0.074 vs. 0.844±0.222, p<0.001; 95% confidence interval [CI] for difference, 0.070-0.116) and SAM 2.0 (0.937±0.074 vs. 0.824±0.236, p<0.001; 95% CI for difference, 0.088-0.138), with a 3-fold reduction in variability (standard deviation: 0.074 vs. 0.222). External validation on the publicly available Spine Endoscopic Atlas dataset (202 images from 10 patients) confirmed generalizability, with MedSAM 2.1 achieving Dice 0.918±0.083, compared to 0.724±0.281 for SAM 2.1 and 0.708±0.290 for SAM 2.0 (both p<0.001). Interrater agreement between 2 independent annotators demonstrated excellent reliability (intraclass correlation coefficient, 0.991; 95% CI, 0.986-0.994; mean pairwise Dice, 0.953±0.051). The MedSAM 2.1-assisted workflow reduced annotation time by 75.8% compared to manual polygon annotation (7.7±13.3 seconds vs. 31.8±18.2 seconds per image; p<0.001; 4.1-fold speedup). SAM 2.1 improved progressively with additional clicks (Dice 0.844 to 0.943 at 10 clicks), while MedSAM 2.1 showed nonmonotonic progression (0.937 to 0.947), indicating near-optimal first-click predictions.
conclusionDomain-adapted MedSAM 2.1 improved single-click annotation accuracy and efficiency for surgical instruments in spinal endoscopy, suggesting that low-resource domain adaptation may facilitate institutional dataset construction for surgical artificial intelligence research.
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