# ROBUST-MIPS: A Combined Skeletal Pose and Instance Segmentation Dataset for Laparoscopic Surgical Instruments

arXiv: https://arxiv.org/abs/2508.21096 · HTML: https://mlsanity.com/paper/2508.21096/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://mlsanity.com/paper/2303.07399/) (2023)
- [CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80](https://mlsanity.com/paper/2012.12453/) (2020)
- [Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room](https://mlsanity.com/paper/2005.03501/) (2020)
- [ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools](https://mlsanity.com/paper/1706.08126/) (2017)
- [CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery](https://mlsanity.com/paper/2406.16039/) (2024)
- [SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking](https://mlsanity.com/paper/2502.11534/) (2025)
- [Robotic Endoscope Control via Autonomous Instrument Tracking](https://mlsanity.com/paper/2107.02317/) (2021)
- [Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge](https://mlsanity.com/paper/2507.16559/) (2025)
- [Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset](https://mlsanity.com/paper/2210.14771/) (2022)
- [Video-based Surgical Tool-tip and Keypoint Tracking using Multi-frame Context-driven Deep Learning Models](https://mlsanity.com/paper/2501.18361/) (2025)

## Built on by (most cited first, 2 of 2)

- [Grounding Surgical Action Triplets with Instrument Instance Segmentation: A Dataset and Target-Aware Fusion Approach](https://mlsanity.com/paper/2511.00643/) (2025)
- [SurgCalib: Gaussian Splatting-Based Hand-Eye Calibration for Robot-Assisted Minimally Invasive Surgery](https://mlsanity.com/paper/2603.08983/) (2026)

## Abstract (arXiv)

Localisation of surgical tools constitutes a foundational building block for computer-assisted interventional technologies. Works in this field typically focus on training deep learning models to perform segmentation tasks. Performance of learning-based approaches is limited by the availability of diverse annotated data. We argue that skeletal pose annotations are a more efficient annotation approach for surgical tools, striking a balance between richness of semantic information and ease of annotation, thus allowing for accelerated growth of available annotated data. To encourage adoption of this annotation style, we present, ROBUST-MIPS, a combined tool pose and tool instance segmentation dataset derived from the existing ROBUST-MIS dataset. Our enriched dataset facilitates the joint study of these two annotation styles and allow head-to-head comparison on various downstream tasks. To demonstrate the adequacy of pose annotations for surgical tool localisation, we set up a simple benchmark using popular pose estimation methods and observe high-quality results. To ease adoption, together with the dataset, we release our benchmark models and custom tool pose annotation software.