README.md

metadata

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 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.

Usage

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. 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.