MISAW-Seg: Pixel-level Surgical Tool Segmentation in Microsurgical Anastomosis

This dataset was presented in the paper: Microsurgical Instrument Segmentation for Robot-Assisted Surgery.

Dataset Overview

MISAW-Seg is a surgical segmentation dataset that extends the original MISAW dataset by introducing segmentation annotations for microsurgical tools involved in artificial vessel anastomosis tasks. The original dataset included kinematic data, workflow annotations, and stereo video recordings, but lacked pixel-wise annotations for surgical tools.

This dataset provides new segmentation masks created using the Roboflow platform, enabling segmentation research in microsurgical environments. MISAW-Seg is released under the license CC BY-NC 4.0.

Data Preparation

The MISAW-Seg dataset is constructed by extending the original MISAW dataset, which consists of microsurgical training sessions involving artificial vessel anastomosis.

The original MISAW dataset includes kinematic data, stereo video, and workflow annotations. These additional components can be accessed separately at:

Download MISAW dataset

In this dataset, we focus on providing:

• Extracted image frames (460×540 px) from stereo microscope video
• Corresponding semantic segmentation masks in both PNG and COCO format

Data Details

Each directory in MISAW-Seg stores raw image data, segmentation masks, and annotation files.

• Directory Structure

  MISAW-Seg/
  ├── images/
  │   ├── 1_1/
  │   │   ├── 1_1_000000.png
  │   │   └── ...
  │   ├── ...
  ├── masks/
  │   ├── 1_1_000000.png
  │   └── ...
  ├── _annotations.coco.json
  ├── fig/
  └── README.md
  

• Image Format

  • Extracted from stereo-microscope videos (original resolution: 960×540 px)
  • Left side was cropped to generate 460×540 px frames
  • Frame rate: 30 fps

• Annotation Formats

  1. COCO Format (_annotations.coco.json)

     The COCO-style annotation file contains polygon-based segmentations, bounding boxes, and category IDs for each object.

  2. PNG Format (masks/)

     Pixel-level segmentation masks are provided in PNG format. Each mask shares the same filename as the corresponding image (e.g., 1_1_000030.png) and stores class IDs as pixel values.

• Segmentation Classes

  The dataset contains the following classes:

  Class Number	Class Name	RGB Color
  0	Left artificial vessel	(0, 255, 0)
  1	Left needle holder	(255, 255, 0)
  2	Needle	(0, 255, 255)
  3	Right artificial vessel	(0, 0, 255)
  4	Right needle holder	(255, 0, 0)
  5	Wire	(255, 0, 255)

Examples of Labeled Data

Figures 1, 2, and 3 show examples of the dataset with segmentation labels.

Figure 1	Figure 2	Figure 3

Citation

If you use this dataset in your research, please cite:

@misc{jeong2025microsurgicalinstrumentsegmentationrobotassisted,
      title={Microsurgical Instrument Segmentation for Robot-Assisted Surgery}, 
      author={Tae Kyeong Jeong and Garam Kim and Juyoun Park},
      year={2025},
      eprint={2509.11727},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.11727}, 
}