# Unsupervised Surgical Instrument Segmentation via Anchor Generation and Semantic Diffusion

arXiv: https://arxiv.org/abs/2008.11946 · HTML: https://mlsanity.com/paper/2008.11946/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 14 of 14)

- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [Latent Graph Representations for Critical View of Safety Assessment](https://mlsanity.com/paper/2212.04155/) (2022)
- [From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments](https://mlsanity.com/paper/2211.16200/) (2022)
- [FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation](https://mlsanity.com/paper/2202.08141/) (2022)
- [Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art](https://mlsanity.com/paper/2304.13014/) (2023)
- [Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation](https://mlsanity.com/paper/2103.01593/) (2021)
- [Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation](https://mlsanity.com/paper/2108.03534/) (2021)
- [SegMatch: A semi-supervised learning method for surgical instrument segmentation](https://mlsanity.com/paper/2308.05232/) (2023)
- [A comprehensive survey on recent deep learning-based methods applied to surgical data](https://mlsanity.com/paper/2209.01435/) (2022)
- [SUPRA: Superpixel Guided Loss for Improved Multi-modal Segmentation in Endoscopy](https://mlsanity.com/paper/2211.04658/) (2022)
- [Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning View](https://mlsanity.com/paper/2408.14789/) (2024)
- [AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument Segmentation](https://mlsanity.com/paper/2411.03695/) (2024)
- [SAF-IS: a Spatial Annotation Free Framework for Instance Segmentation of Surgical Tools](https://mlsanity.com/paper/2309.01723/) (2023)
- [Label-supervised surgical instrument segmentation using temporal equivariance and semantic continuity](https://mlsanity.com/paper/2403.09551/) (2024)

## Abstract (arXiv)

Surgical instrument segmentation is a key component in developing context-aware operating rooms. Existing works on this task heavily rely on the supervision of a large amount of labeled data, which involve laborious and expensive human efforts. In contrast, a more affordable unsupervised approach is developed in this paper. To train our model, we first generate anchors as pseudo labels for instruments and background tissues respectively by fusing coarse handcrafted cues. Then a semantic diffusion loss is proposed to resolve the ambiguity in the generated anchors via the feature correlation between adjacent video frames. In the experiments on the binary instrument segmentation task of the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset, the proposed method achieves 0.71 IoU and 0.81 Dice score without using a single manual annotation, which is promising to show the potential of unsupervised learning for surgical tool segmentation.