  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 employs a three-pronged approach: extracting boundaries
directly from the optical flow, selectively discarding frames with inferior
flow quality, and employing a fine-tuning process with variable frame rates. We
thoroughly evaluate our strategy on the EndoVis2017 VOS dataset and Endovis2017
Challenge dataset, where our model demonstrates promising results, achieving a
mean Intersection-over-Union (mIoU) of 0.75 and 0.72, respectively. Our
findings suggest that our approach can greatly decrease the need for manual
annotations in clinical environments and may facilitate the annotation process
for new datasets. The code is available at
https://github.com/wpr1018001/Rethinking-Low-quality-Optical-Flow.git
