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.

This segmentation dataset was developed by the Korea Institute of Science and Technology (KIST) by annotating the original MISAW dataset.

Data Preparation

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

In this dataset, we focus on providing:

• Extracted image frames: 460 × 540 px frames cropped from the original stereo microscope video.
• Corresponding semantic segmentation masks: Provided in both PNG (bitmask) and COCO (JSON) formats.

Technical Specifications:

• Source Dataset: MISAW (Microsurgical Anastomosis Sub-task Dataset)
• Annotation Tool: Manual annotation via the Roboflow platform.
• Task: Surgical instrument segmentation in artificial vessel anastomosis.

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}, 
}

Acknowledgment

This work was supported by the Technology Innovation Program (No. RS-2024-00443054) grant funded by the Korea government (the Ministry of Trade, Industry & Energy (MOTIE)).

• Project Name: Development of a Supermicrosurgical Robot System for Sub-0.8mm Vessel Anastomosis through Human-Robot Autonomous Collaboration in Surgical Workflow Recognition
• Project Number: RS-2024-00443054