---
dataset_info:
  features:
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: image_id
      dtype: string
  splits:
    - name: train
      num_examples: 245
    - name: test
      num_examples: 62
task_categories:
  - image-segmentation
task_ids:
  - semantic-segmentation
tags:
  - medical-imaging
  - surgical
  - endoscopy
  - segmentation
size_categories:
  - n<1K
license: cc-by-nc-sa-4.0
---

# m2caiSeg - Surgical Scene Segmentation Dataset

Multi-class semantic segmentation dataset for surgical instrument and anatomy segmentation from the m2cai16 challenge.

## Dataset Preview

<dataset-viewer />

## Dataset Details

| Property | Value |
|---|---|
| **Source** | m2cai16 Challenge |
| **Modality** | Endoscopy (RGB) |
| **Task** | Multi-class Semantic Segmentation |
| **Classes** | 19 (1 background + 17 foreground + 1 unknown) |
| **Train** | 245 images |
| **Test** | 62 images |
| **Image Format** | JPEG |
| **Mask Format** | PNG (single-channel, integer class labels 0-18) |

## Class Mapping (19 Classes)

Masks are **single-channel grayscale PNGs** where each pixel value is a class ID:

| Class ID | Class Name |
|----------|-----------|
| 0 | background |
| 1 | grasper |
| 2 | bipolar |
| 3 | hook |
| 4 | scissors |
| 5 | clipper |
| 6 | irrigator |
| 7 | specimen-bag |
| 8 | trocars |
| 9 | clip |
| 10 | liver |
| 11 | gall-bladder |
| 12 | fat |
| 13 | upperwall |
| 14 | artery |
| 15 | intestine |
| 16 | bile |
| 17 | blood |
| 18 | unknown |

The full mapping is also available as [`class_mapping.json`](class_mapping.json).

## Usage

```python
from datasets import load_dataset
import numpy as np

# Load from HuggingFace
dataset = load_dataset("Angelou0516/m2caiSeg")

# Access a sample
sample = dataset["train"][0]
image = sample["image"]       # PIL Image (RGB)
mask = sample["mask"]          # PIL Image (grayscale, pixel values = class IDs)
image_id = sample["image_id"] # e.g., "0"

# Get class labels
mask_array = np.array(mask.convert("L"))
unique_classes = np.unique(mask_array)  # subset of 0-18
```

## License & Attribution

Released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
The segmentation annotations are from the m2caiSeg dataset; the underlying video
frames are from the m2cai16 challenge (CAMMA, University of Strasbourg), released
under CC BY-NC-SA 4.0. Cite both:

- Maqbool, S., Riaz, A., Sajid, H., Hasan, O. "m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks." arXiv:2008.10134, 2020.
- Twinanda, A. P., et al. "EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos." IEEE Transactions on Medical Imaging, 36(1):86-97, 2017. (m2cai16 / Cholec source, CAMMA)

**Changes made in this mirror:** the m2caiSeg images and class masks are repackaged
into HuggingFace parquet with train/test splits; a `class_mapping.json` index was
added. No pixel data or class labels were altered.