ArticleInternational journal of computer assisted radiology and surgery2026
Grounding surgical action triplets with instrument instance segmentation: a dataset and target-aware fusion approach.
Article in International journal of computer assisted radiology and surgery, 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
purposeUnderstanding surgical instrument-tissue interactions requires not only identifying which instrument performs which action on which anatomical target, but also grounding these interactions spatially within the surgical scene. Existing surgical action triplet recognition methods are limited to learning from frame-level classification, failing to reliably link actions to specific instrument instances. Previous attempts at spatial grounding have primarily relied on class activation maps, which lack the precision and robustness required for detailed instrument-tissue interaction analysis. To address this gap, we propose grounding surgical action triplets with instrument instance segmentation, or triplet segmentation for short, a new unified task which produces spatially grounded
methodsWe start by presenting CholecTriplet-Seg, a large-scale dataset containing over 30,000 annotated frames, linking instrument instance masks with action verb and anatomical target annotations, and establishing the first benchmark for strongly supervised, instance-level triplet grounding and evaluation. To learn triplet segmentation, we propose TargetFusionNet, a novel architecture that extends Mask2Former with a target-aware fusion mechanism to address the challenge of accurate anatomical target prediction by fusing weak anatomy priors with instrument instance queries.
resultsEvaluated across recognition, detection, and triplet segmentation metrics, TargetFusionNet consistently improves performance over existing baselines, demonstrating that strong instance supervision combined with weak target priors significantly enhances the accuracy and robustness of surgical action understanding.
conclusionTriplet segmentation establishes a unified framework for spatially grounding surgical action triplets. The proposed CholecTriplet-Seg benchmark and TargetFusionNet architecture pave the way for more interpretable, fine-grained surgical scene understanding.
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