Motion-Boundary-Driven Unsupervised Surgical Instrument Segmentation in Low-Quality Optical Flow

Yang Liu, Peiran Wu, Jiayu Huo, Gongyu Zhang, Zhen Yuan, Christos Bergeles, Rachel Sparks, Prokar Dasgupta, Alejandro Granados, Sébastien Ourselin

Abstract: Unsupervised video-based surgical instrument segmentation has the potential to accelerate the adoption of robot-assisted procedures by reducing the reliance on manual annotations. However, the generally low quality of optical flow in endoscopic footage poses a great challenge for unsupervised methods that rely heavily on motion cues. To overcome this limitation, we propose a novel approach that pinpoints motion boundaries, regions with abrupt flow changes, while selectively discarding frames with globally low-quality flow and adapting to varying motion patterns. Experiments on the EndoVis2017 VOS and EndoVis2017 Challenge datasets show that our method achieves mean Intersection-over-Union (mIoU) scores of 0.75 and 0.72, respectively, effectively alleviating the constraints imposed by suboptimal optical flow. This enables a more scalable and robust surgical instrument segmentation solution in clinical settings. The code is publicly available at https://github.com/wpr1018001/Rethinking-Low-quality-Optical-Flow.git.