WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity
<|reference_start|>WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity: For robotic surgical videos, instrument presence annotations are typically recorded with video streams, which offering the potential to reduce the manually annotated costs for segmentation. However, weakly supervised surgical instrument segmentation with only instrument presence labels has been rarely explored in surgical domain due to the highly under-constrained challenges. Temporal properties can enhance representation learning by capturing sequential dependencies and patterns over time even in incomplete supervision situations. From this, we take the inherent temporal attributes of surgical video into account and extend a two-stage weakly supervised segmentation paradigm from different perspectives. Firstly, we make temporal equivariance constraint to enhance pixel-wise temporal consistency between adjacent features. Secondly, we constrain class-aware semantic continuity between global and local regions across temporal dimension. Finally, we generate temporal-enhanced pseudo masks from consecutive frames to suppress irrelevant regions. Extensive experiments are validated on two surgical video datasets, including one cholecystectomy surgery benchmark and one real robotic left lateral segment liver surgery dataset. We annotate instance-wise instrument labels with fixed time-steps which are double checked by a clinician with 3-years experience to evaluate segmentation results. Experimental results demonstrate the promising performances of our method, which consistently achieves comparable or favorable results with previous state-of-the-art approaches.<|reference_end|>
@article{wang2024weaksurg:,
    title={WeakSurg: Weakly supervised surgical instrument segmentation using
  temporal equivariance and semantic continuity},
    author={Qiyuan Wang and Yanzhe Liu and Shang Zhao and Rong Liu and S. Kevin
  Zhou},
    journal={arXiv preprint arXiv:2403.09551},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09551},
    primaryClass={cs.CV}
}