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Surgical instrument segmentation is a key component in developingcontext-aware operating rooms. Existing works on this task heavily rely on thesupervision of a large amount of labeled data, which involve laborious andexpensive human efforts. In contrast, a more affordable unsupervised approachis developed in this paper. To train our model, we first generate anchors aspseudo labels for instruments and background tissues respectively by fusingcoarse handcrafted cues. Then a semantic diffusion loss is proposed to resolvethe ambiguity in the generated anchors via the feature correlation betweenadjacent video frames. In the experiments on the binary instrument segmentationtask of the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challengedataset, the proposed method achieves 0.71 IoU and 0.81 Dice score withoutusing a single manual annotation, which is promising to show the potential ofunsupervised learning for surgical tool segmentation.

Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion