TL;DR
This paper introduces a joint generation and segmentation approach that improves the generalization of surgical instrument segmentation models across different datasets and unlabelled data domains, addressing the limitations of existing deep learning methods.
Contribution
The paper proposes a novel joint generation and segmentation strategy that leverages labelled data from one domain to improve segmentation in unlabelled domains, enhancing generalization capabilities.
Findings
High mean Dice scores on both labelled and unlabelled datasets
Outperforms state-of-the-art methods in generalization
Effective on real robot-assisted surgery videos
Abstract
Surgical instrument segmentation for robot-assisted surgery is needed for accurate instrument tracking and augmented reality overlays. Therefore, the topic has been the subject of a number of recent papers in the CAI community. Deep learning-based methods have shown state-of-the-art performance for surgical instrument segmentation, but their results depend on labelled data. However, labelled surgical data is of limited availability and is a bottleneck in surgical translation of these methods. In this paper, we demonstrate the limited generalizability of these methods on different datasets, including human robot-assisted surgeries. We then propose a novel joint generation and segmentation strategy to learn a segmentation model with better generalization capability to domains that have no labelled data. The method leverages the availability of labelled data in a different domain. The…
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