dataset card

README.md

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+---
+pretty_name: GenSegDataset
+license: other
+license_name: mixed-per-subset
+task_categories:
+- image-segmentation
+task_ids:
+- semantic-segmentation
+tags:
+- medical
+- medical-imaging
+- 2d-segmentation
+- semantic-segmentation
+- benchmark
+- multi-modality
+size_categories:
+- 10K<n<100K
+# NOTE: the `configs:` data_files mapping below is finalized once the on-disk
+# format (Parquet vs PNG mirror) is chosen; left as a template for now.
+# configs:
+# - config_name: cvc_clinicdb
+#   data_files:
+#   - split: train
+#     path: cvc_clinicdb/official/train/*
+---
+
+# GenSegDataset — A Unified 2D Medical Image Segmentation Benchmark
+
+**GenSegDataset** is a standardized collection of **10 public 2D medical image
+segmentation datasets** spanning **8 imaging modalities**, re-packaged into a single
+consistent layout (identical directory structure, mask encoding, split files, and
+metadata) so that segmentation models — and generative mask-conditioned data
+augmentation methods — can be trained and compared across modalities with one data
+pipeline.
+
+> This repository redistributes **standardized derivatives** of existing public
+> datasets. Each subset retains the license and citation requirements of its
+> original source — see [Licensing & Attribution](#licensing--attribution) and
+> please cite the original works.
+
+---
+
+## Overview
+
+| Subset | Modality | Anatomy / Target | Classes | Channels | Sample size | Protocol | Train / Val / Test |
+|---|---|---|---|---|---|---|---|
+| `cvc_clinicdb` | Colonoscopy | Polyp | 2 | RGB | 384×288 | official | 490 / 61 / 61 |
+| `kvasir_seg` | GI endoscopy | Polyp | 2 | RGB | ~622×529 (var) | official | 800 / 100 / 100 |
+| `fives` | Retinal fundus | Vessel | 2 | RGB | 2048×2048 | official | 480 / 120 / 200 |
+| `busi` | Breast ultrasound | Tumor | 2 | RGB | variable | 5-fold (fold01–05) | 545 / 78 / 157 (fold01) |
+| `refuge2` | Retinal fundus | Optic disc & cup | 3 | RGB | ~2124×2056 | official | 400 / 400 / 400 |
+| `acdc_png` | Cardiac MRI (2D slices) | RV / Myo / LV | 4 | grayscale | ~240×256 (var) | official | 136 / 210 / 380 |
+| `idridd_segmentation` | Retinal fundus | Diabetic-retinopathy lesions | 6 | RGB | 4288×2848 | 5-fold (fold01–05) | 43 / 11 / 27 (fold01) |
+| `pannuke_semantic` | Histopathology (H&E) | Nuclei (5 types) | 6 | RGB | 256×256 | 3-fold (fold01–03) | 2722 / 2523 / 2656 (fold01) |
+| `medsegdb_isic2018` | Dermoscopy | Skin lesion | 2 | RGB | 256×256 | holdout | 2582 / 369 / 737 |
+| `medsegdb_kits19` | Kidney CT (2D slices) | Kidney region (binary) | 2 | grayscale¹ | 256×256 | 5-fold (fold01–05) | 2832 / 479 / 705 (fold01) |
+
+¹ `medsegdb_kits19` images are grayscale in content but stored as 3-channel PNG;
+read them as grayscale (`IMREAD_GRAYSCALE`) for true single-channel input.
+
+For cross-validation subsets (`busi`, `idridd_segmentation`, `medsegdb_kits19`:
+5 folds; `pannuke_semantic`: 3 folds) every fold reuses the **same images** under a
+different train/val/test partition. `holdout` and `official` provide a single fixed
+partition.
+
+---
+
+## Directory layout
+
+Each subset is shipped as a single archive **`<subset>.tar`** at the repo root;
+extracting it yields the structure below.
+
+```
+GenSegDataset/
+  <subset>.tar               # download & extract -> <subset>/...
+  <subset>/                  # (after extraction)
+    metadata.json            # subset-level metadata
+    manifest.jsonl           # one JSON line per image: relative image/mask paths
+    <protocol>/              # e.g. official | fold01..fold05 | fold01..fold03 | holdout
+      train/ val/ test/
+        images/              # input images (.png)
+        masks/               # segmentation masks (.png)
+  README.md                  # this card
+```
+
+- **Pairing**: an image and its mask share the same file stem
+  (`images/<id>.png` ↔ `masks/<id>.png`); `manifest.jsonl` also lists the pairing
+  explicitly with paths relative to the subset root.
+- **Modality / channels**: input images are RGB (3-channel) except `acdc_png`
+  (true grayscale) and `medsegdb_kits19` (grayscale content, 3-channel container).
+
+### Mask encoding
+
+Masks are single-channel `uint8` label maps with **values `0 … C-1`**
+(`0` = background), **not** 0/255. Semantic meaning per index (confirmed against each
+subset's `metadata.json` and the standardization scripts):
+
+| Subset | Class indices |
+|---|---|
+| `cvc_clinicdb`, `kvasir_seg` | 0 background · 1 polyp |
+| `fives` | 0 background · 1 vessel |
+| `busi` | 0 background · 1 tumor (multi-instance masks merged) |
+| `medsegdb_isic2018` | 0 background · 1 lesion |
+| `medsegdb_kits19` | 0 background · 1 foreground (kidney region, binary) |
+| `refuge2` | 0 background · 1 optic disc · 2 optic cup |
+| `acdc_png` | 0 background · 1 right ventricle · 2 myocardium · 3 left ventricle |
+| `idridd_segmentation` | 0 background · 1 microaneurysms · 2 haemorrhages · 3 hard exudates · 4 soft exudates · 5 optic disc |
+| `pannuke_semantic` | 0 background · 1 neoplastic · 2 inflammatory · 3 connective · 4 dead · 5 epithelial |
+
+---
+
+## Usage
+
+### Download & extract a subset
+
+```python
+from huggingface_hub import hf_hub_download
+import tarfile
+
+p = hf_hub_download("GenSegDataset/GenSegDataset", "cvc_clinicdb.tar", repo_type="dataset")
+tarfile.open(p).extractall("GenSegDataset")        # -> GenSegDataset/cvc_clinicdb/...
+```
+
+### Direct file access (after extraction)
+
+```python
+import cv2, glob, os
+
+root = "GenSegDataset/cvc_clinicdb/official/train"
+img = cv2.imread(f"{root}/images/0001.png")                       # RGB input
+msk = cv2.imread(f"{root}/masks/0001.png", cv2.IMREAD_GRAYSCALE)  # label map 0..C-1
+```
+
+### With `datasets` (once the Parquet/loader build is published)
+
+```python
+from datasets import load_dataset
+ds = load_dataset("GenSegDataset/GenSegDataset", "cvc_clinicdb")   # config = subset
+sample = ds["train"][0]            # {"image": PIL.Image, "mask": PIL.Image, ...}
+```
+
+> The `datasets`-loadable build (Parquet with embedded image/mask + a config per
+> subset) is added on top of the raw file mirror; until then use direct file access.
+
+---
+
+## Standardization methodology
+
+All subsets were converted to the unified layout above with a shared pipeline:
+
+1. **Format unification** — images/masks re-encoded to `.png`; masks remapped to a
+   contiguous `0 … C-1` label space.
+2. **Fixed, reproducible splits** — official splits used where they exist
+   (`cvc_clinicdb`, `kvasir_seg`, `fives`, `refuge2`, `acdc_png`); otherwise fixed
+   k-fold (`busi`, `idridd_segmentation`, `medsegdb_kits19`: 5-fold;
+   `pannuke_semantic`: official 3-fold) or a fixed holdout (`medsegdb_isic2018`).
+   Splits are frozen in `manifest.jsonl` so results are reproducible.
+3. **Metadata** — each subset carries `metadata.json` and a per-image
+   `manifest.jsonl`.
+
+The collection was assembled to benchmark (a) 2D segmentation backbones and
+(b) generative, mask-conditioned data-augmentation methods under one consistent
+interface.
+
+---
+
+## Licensing & Attribution
+
+This repository contains **standardized derivatives** of the datasets below. **Each
+subset is governed by its original license**; users must comply with the source
+terms and **cite the original publications**. Source links:
+
+| Subset | Source |
+|---|---|
+| `cvc_clinicdb` | CVC-ClinicDB (Bernal et al., 2015) |
+| `kvasir_seg` | Kvasir-SEG (Jha et al., 2020) |
+| `fives` | FIVES (Jin et al., 2022) |
+| `busi` | BUSI (Al-Dhabyani et al., 2020) |
+| `refuge2` | REFUGE / REFUGE2 (Orlando et al., 2020; Fang et al., 2022) |
+| `acdc_png` | ACDC (Bernard et al., 2018) |
+| `idridd_segmentation` | IDRiD (Porwal et al., 2018, 2020) |
+| `pannuke_semantic` | PanNuke (Gamper et al., 2019, 2020) |
+| `medsegdb_isic2018` | ISIC 2018 / HAM10000 (Codella et al., 2019; Tschandl et al., 2018) |
+| `medsegdb_kits19` | KiTS19 (Heller et al., 2019, 2021) |
+
+---
+
+## Citation
+
+If you use **GenSegDataset**, please cite this collection **and** the original
+source dataset(s) you use.
+
+```bibtex
+@misc{gensegdataset2026,
+  title        = {GenSegDataset: A Unified 2D Medical Image Segmentation Benchmark},
+  author       = {<authors>},
+  year         = {2026},
+  howpublished = {Hugging Face Datasets},
+  note         = {Standardized collection of 10 public 2D medical segmentation datasets}
+}
+```
+
+<details>
+<summary>Original-source BibTeX (please verify before camera-ready)</summary>
+
+```bibtex
+@article{bernal2015cvcclinicdb,
+  title={WM-DOVA maps for accurate polyp highlighting in colonoscopy},
+  author={Bernal, Jorge and S{\'a}nchez, F Javier and Fern{\'a}ndez-Esparrach, Gloria and Gil, Debora and Rodr{\'i}guez, Cristina and Vilari{\~n}o, Fernando},
+  journal={Computerized Medical Imaging and Graphics}, volume={43}, pages={99--111}, year={2015}}
+
+@inproceedings{jha2020kvasirseg,
+  title={Kvasir-SEG: A segmented polyp dataset},
+  author={Jha, Debesh and Smedsrud, Pia H and Riegler, Michael A and Halvorsen, P{\aa}l and de Lange, Thomas and Johansen, Dag and Johansen, H{\aa}vard D},
+  booktitle={MultiMedia Modeling (MMM)}, year={2020}}
+
+@article{jin2022fives,
+  title={FIVES: A fundus image dataset for artificial intelligence based vessel segmentation},
+  author={Jin, Kai and Huang, Xingru and Zhou, Jingxing and others},
+  journal={Scientific Data}, volume={9}, year={2022}}
+
+@article{aldhabyani2020busi,
+  title={Dataset of breast ultrasound images},
+  author={Al-Dhabyani, Walid and Gomaa, Mohammed and Khaled, Hussien and Fahmy, Aly},
+  journal={Data in Brief}, volume={28}, year={2020}}
+
+@article{orlando2020refuge,
+  title={REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs},
+  author={Orlando, Jos{\'e} Ignacio and Fu, Huazhu and others},
+  journal={Medical Image Analysis}, volume={59}, year={2020}}
+
+@article{bernard2018acdc,
+  title={Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?},
+  author={Bernard, Olivier and Lalande, Alain and others},
+  journal={IEEE Transactions on Medical Imaging}, volume={37}, number={11}, year={2018}}
+
+@article{porwal2020idrid,
+  title={IDRiD: Diabetic retinopathy -- segmentation and grading challenge},
+  author={Porwal, Prasanna and Pachade, Samiksha and others},
+  journal={Medical Image Analysis}, volume={59}, year={2020}}
+
+@article{gamper2020pannuke,
+  title={PanNuke dataset extension, insights and baselines},
+  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and others},
+  journal={arXiv:2003.10778}, year={2020}}
+
+@article{codella2019isic2018,
+  title={Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the ISIC},
+  author={Codella, Noel and Rotemberg, Veronica and others},
+  journal={arXiv:1902.03368}, year={2019}}
+
+@article{tschandl2018ham10000,
+  title={The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions},
+  author={Tschandl, Philipp and Rosendahl, Cliff and Kittler, Harald},
+  journal={Scientific Data}, volume={5}, year={2018}}
+
+@article{heller2021kits19,
+  title={The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge},
+  author={Heller, Nicholas and Isensee, Fabian and others},
+  journal={Medical Image Analysis}, volume={67}, year={2021}}
+```
+
+</details>
+
+---
+
+## Maintenance notes
+
+- Class indices/names above were confirmed from each subset's `metadata.json`,
+  the actual mask label values, and the standardization scripts
+  (`tools/process_downloaded_segmentation_datasets.py`). Most subsets' `metadata.json`
+  omits an explicit `num_classes`/`modality` field (the two `medsegdb_*` subsets
+  include them).
+- Read `acdc_png` and `medsegdb_kits19` as grayscale even though some files are
+  stored as 3-channel containers.