| --- |
| 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. |
|
|
