arXiv subjects

Alex Y. W. Kong

Publications and source records attributed to Alex Y. W. Kong.

2 recordsLinked to original sources

arXiv2026

SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking

Surgical scene understanding demands temporally consistent tracking of instruments and tissues. For clinical use, such tracking should generalize to new centers and procedure types, yet retraining for each of them is costly and not scalable. Interactive video object segmentation offers a way toward this generalization: the target is specified at inference by a first-frame visual prompt, so a model can generalize to unseen categories and new scenarios without retraining. However, training such a generalizable model demands spatio-temporal masklet annotations at a scale and procedural diversity that existing surgical benchmarks lack. We fill this gap with iSurg, the largest surgical segmentation benchmark to our knowledge, spanning six procedure types with over 170k frames, 410k object masks, and 2.4k masklets, including an in-house clinical dataset of four 30-minute videos. Yet tracking over such long videos remains challenging: the target needs to be re-identified among visually similar objects after long absences, while long-term memory has to judge whether each stored frame genuinely depicts the target, both demanding a stable object-level semantic identity. To this end, we propose SurgSLOT, a surgical segmentation generalist that segments any prompted target through two coupled modules built on this semantic identity: Temporal Semantic Learning learns it for re-identification, and Semantic-driven Long-term Memory reuses it to select reliable memory frames, suppressing identity drift over long procedures. On the SAM2 and SAM3 backbones, SurgSLOT reaches 81.0 and 82.8 Macro Average J&F under cross-dataset evaluation, surpassing their fine-tuned counterparts by 5.1 and 5.3 points and transferring zero-shot to an unseen procedure type and unseen object categories, with the SAM2 version running in real time at 68 FPS.

cs.CV↗

arXiv2026

UniSurgSAM: A Unified Promptable Model for Reliable Surgical Video Segmentation

Surgical video segmentation is fundamental to computer-assisted surgery. In practice, surgeons need to dynamically specify targets throughout extended procedures, using heterogeneous cues such as visual selections, textual expressions, or audio instructions. However, existing Promptable Video Object Segmentation (PVOS) methods are typically restricted to a single prompt modality and rely on coupled frameworks that cause optimization interference between target initialization and tracking. Moreover, these methods produce hallucinated predictions when the target is absent and suffer from accumulated mask drift without failure recovery. To address these challenges, we present UniSurgSAM, a unified PVOS model enabling reliable surgical video segmentation through visual, textual, or audio prompts. Specifically, UniSurgSAM employs a decoupled two-stage framework that independently optimizes initialization and tracking to resolve the optimization interference. Within this framework, we introduce three key designs for reliability: presence-aware decoding that models target absence to suppress hallucinations; boundary-aware long-term tracking that prevents mask drift over extended sequences; and adaptive state transition that closes the loop between stages for failure recovery. Furthermore, we establish a multi-modal and multi-granular benchmark from four public surgical datasets with precise instance-level masklets. Extensive experiments demonstrate that UniSurgSAM achieves state-of-the-art performance in real time across all prompt modalities and granularities, providing a practical foundation for computer-assisted surgery. Code and datasets will be available at https://jinlab-imvr.github.io/UniSurgSAM.

eess.IV↗