# Robust Medical Instrument Segmentation Challenge 2019

arXiv: https://arxiv.org/abs/2003.10299 · HTML: https://mlsanity.com/paper/2003.10299/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 20 of 20)

- [A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation](https://mlsanity.com/paper/2107.12435/) (2021)
- [From Forks to Forceps: A New Framework for Instance Segmentation of Surgical Instruments](https://mlsanity.com/paper/2211.16200/) (2022)
- [Text Promptable Surgical Instrument Segmentation with Vision-Language Models](https://mlsanity.com/paper/2306.09244/) (2023)
- [Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge](https://mlsanity.com/paper/2202.12031/) (2022)
- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets](https://mlsanity.com/paper/2204.05235/) (2022)
- [CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery](https://mlsanity.com/paper/2406.16039/) (2024)
- [Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation](https://mlsanity.com/paper/2103.01593/) (2021)
- [Task Fingerprinting for Meta Learning in Biomedical Image Analysis](https://mlsanity.com/paper/2107.03949/) (2021)
- [Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset](https://mlsanity.com/paper/2210.14771/) (2022)
- [SegMatch: A semi-supervised learning method for surgical instrument segmentation](https://mlsanity.com/paper/2308.05232/) (2023)
- [Multitask Learning in Minimally Invasive Surgical Vision: A Review](https://mlsanity.com/paper/2401.08256/) (2024)
- [Multiscale Memory Comparator Transformer for Few-Shot Video Segmentation](https://mlsanity.com/paper/2307.07812/) (2023)
- [Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models](https://mlsanity.com/paper/2403.17192/) (2024)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control](https://mlsanity.com/paper/2507.15292/) (2025)
- [SurgPIS: Surgical-instrument-level Instances and Part-level Semantics for Weakly-supervised Part-aware Instance Segmentation](https://mlsanity.com/paper/2507.19592/) (2025)
- [RobustSurg: Tackling domain generalisation for out-of-distribution surgical scene segmentation](https://mlsanity.com/paper/2512.02188/) (2025)
- [GroundedSurg: A Multi-Procedure Benchmark for Language-Conditioned Surgical Tool Segmentation](https://mlsanity.com/paper/2603.01108/) (2026)
- [A Surgical Foundation Model Reveals Task-Dependent Label Efficiency](https://mlsanity.com/paper/2609.31821/) (2026)

## Abstract (arXiv)

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).