README.md

---
license: cc-by-nc-4.0
task_categories:
- image-segmentation
- image-classification
tags:
- medical
- surgical
- microsurgery
---

# MAVIS (Micro-surgical Artificial Vascular anastomosIS)

This dataset was presented in the paper: [SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding](https://huggingface.co/papers/2511.21339).

## Dataset Overview

*MAVIS* is a microsurgical dataset comprising 19 videos of artificial vascular anastomosis procedures performed by three expert microsurgeons at College of Medicine, Korea University, Republic of Korea.  
For each video frame, it provides:

- **Pixel-level segmentation** of seven tool categories
- **Frame-level workflow annotations**: surgical stage, phase, and step

This dataset supports research on surgical tool segmentation and surgical workflow recognition in microsurgical environments.
<!--
## Data Collection

1. **Subjects & Cases**  
   - 19 recorded anastomosis sessions on an artificial vessel simulator  
   - Surgeon assignments:  
     - CASE 01–07: Surgeon 1 
     - CASE 08–14: Surgeon 2 
     - CASE 15–19: Surgeon 3 

2. **Acquisition Setup**  
   - Microscope/Camera model: ???  
   - Original resolution: ??? px  
   - Frame rate: ??? fps  
   - Cropped frame size: 1920 × 1072 px  

3. **Annotation Tools & Process**  
   - **Segmentation**: ??? 
   - **Workflow**: Manual tagging of stage/phase/step by the non-medical 

4. **Annotators**  
   - Labels applied following a standardized workflow guideline 
--> 

## Data Details
Each directory in the *MAVIS* dataset stores raw image data, segmentation masks, and annotation files.

- **Directory Structure**

    ```
    MAVIS/
    ├── frames/
    │   ├── CASE01/
    │   │   ├── image_00001.jpg
    │   │   └── ...
    │   ├── ...
    ├── annotations/
    │   ├── long-term.json
    │   ├── short-term.json
    │   ├── segmentations/
    │   │   ├── CASE01/
    │   │   │   ├── image_00001.png
    │   │   │   └── ...
    │   │   ├── ...
    │   └── segmentations_with_keypoint/
    │       ├── CASE01/
    │       │   ├── image_00001.png
    │       │   └── ...
    │       ├── ...
    ├── fig/
    └── README.md
    ```

- **Annotation Formats**

    - **short-term.json**: polygon mask data for seven tool classes for each frame
    - **long-term.json**: stage, phase, and step labels for each frame


- **Stage–Phase–Step Hierarchy**

    The workflow annotations are structured into six **Stages**, comprising one or more sequential **Phases**, which in turn consist of multiple sequential **Steps**.
    <center> 
    
    | Phase Class ID | Stage Name        | Stage Class ID | Phase Name        | Step Class ID | Step Name         |
    |:--------:|:-----------------:|:--------:|:-----------------:|:--------:|:-----------------:|
    | 0        | First tying       | 0        | Suturing          | 0        | Needle holding    |
    | 1        | Second 120° tying | 1        | Knot tying        | 1        | Needle passing    |
    | 2        | Second 180° tying | 2        | Cutting           | 2        | Needle dropping   |
    | 3        | Front side tying  | 3        | Flip              | 3        | 1st knot          |
    | 4        | Flip              |          |                   | 4        | 2nd knot          |
    | 5        | Back side tying   |          |                   | 5        | 3rd knot          |
    |          |                   |          |                   | 6        | Cutting           |
    |          |                   |          |                   | 7        | Flip clamp        |
    
    </center>

    <details>
    <summary>[Click to expand] Full annotation hierarchy with descriptions</summary>
    
    1. **First tying** (forming the first knot)
       1. **Phase: Suturing** – place and position the suture
          - **Needle holding**: grasp the suture needle securely with the needle holder
          - **Needle passing**: insert the needle through both edges of the vessel and pull it through
          - **Needle dropping**: release the needle at the optimal position for tying (≈5 o’clock)
       2. **Phase: Knot tying** – create the knot
          - **1st knot**: wrap the free end of the suture around the instrument and tighten
          - **2nd knot**: repeat wrapping in the opposite direction and tighten
          - **3rd knot**: final wrap to secure the stitch
       3. **Phase: Cutting** – trim excess suture
          - **Cutting**: use scissors to sever both ends of the suture (can cut both at once or sequentially)
    2. **Second 120° tying** (forming the second knot at a position rotated 120° from the first tying)
       - *Subtasks*: identical to First tying (Suturing → Knot tying → Cutting)
    3. **Second 180° tying** (forming the second knot at a position rotated 180° from the first tying)
       - *Subtasks*: identical to First tying (Suturing → Knot tying → Cutting)
    4. **Front side tying** (additional knot on the front face between first and second)
       - *Subtasks*: identical to First tying (Suturing → Knot tying → Cutting)
    5. **Flip** (reorient vessel for back‐side access)
       - **Phase: Flip** – flip the vessel clamp
         - **Flip clamp**: reposition the clamp so that the vessel’s backside faces the camera
    6. **Back side tying** (forming knots on the backside between first and second)
       - *Subtasks*: identical to First tying (Suturing → Knot tying → Cutting)

    </details>

<!--
| Class ID | Phase Name        |
|----------|-------------------|
| 0        | Suturing          |
| 1        | Knot tying        |
| 2        | Cutting           |
| 3        | Flip              |

| Class ID | Step Name         |
|----------|-------------------|
| 0        | Needle holding    |
| 1        | Needle passing    |
| 2        | Needle dropping   |
| 3        | 1st knot          |
| 4        | 2nd knot          |
| 5        | 3rd knot          |
| 6        | Cutting           |
| 7        | Flip clamp        |
-->


- **Tool Segmentation Classes**

    The dataset contains the following surgical tool classes:
    
    <center>
      
    | Class ID | Class Name       |RGB Color       |
    |:--------:|:----------------:|:--------------:|
    | 0        | forceps          |(253, 0, 26)    |
    | 1        | scissors         |(43, 253, 62)   |
    | 2        | vascular_clamps  |(0, 43, 249)    |
    | 3        | needle_holder    |(255, 253, 66)  |
    | 4        | vessel           |(253, 40, 250)  |
    | 5        | needle           |(38, 255, 254)  |
    | 6        | thread           |(198, 161, 251) |
    
    </center>
  
## Examples of Labeled Data

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

<center>
  
Figure 1 | Figure 2 | Figure 3
:-------------------------:|:-------------------------:|:-------------------------:
![](./fig/ex1.png)  |  ![](./fig/ex2.png) | ![](./fig/ex3.png)

</center>

<!--
<figure>
  <img src="./fig/ex1.png" width="640" height="360"/>
  <figcaption>Figure 1: Example of Segmentation Mask of Image 1</figcaption>
</figure>

<figure>
  <img src="./fig/ex2.png" width="640" height="360"/>
  <figcaption>Figure 2: Example of Segmentation Mask of Image 2</figcaption>
</figure>

<figure>
  <img src="./fig/ex3.png" width="640" height="360"/>
  <figcaption>Figure 3: Example of Segmentation Mask of Image 3</figcaption>
</figure>
-->

## Citation

```bibtex
@misc{choi2025surgmllmbenchmultimodallargelanguage,
      title={SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding}, 
      author={Tae-Min Choi and Tae Kyeong Jeong and Garam Kim and Jaemin Lee and Yeongyoon Koh and In Cheul Choi and Jae-Ho Chung and Jong Woong Park and Juyoun Park},
      year={2025},
      eprint={2511.21339},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.21339}, 
}
```