Rethinking Low-quality Optical Flow in Unsupervised Surgical Instrument Segmentation

arXiv:2403.10039v1 Announce Type: new
Abstract: Video-based surgical instrument segmentation plays an important role in robot-assisted surgeries. Unlike supervised settings, unsupervised segmentation relies heavily on motion cues, which are challenging to discern due to the typically lower quality of optical flow in surgical footage compared to natural scenes. This presents a considerable burden for the advancement of unsupervised segmentation techniques. In our work, we address the challenge of enhancing model performance despite the inherent limitations of low-quality optical flow. Our methodology …

arxiv cs.ai cs.cv flow low optical optical flow quality segmentation type unsupervised