Datasets:

MaybeRichard
/

GenSegDataset

@@ -1,275 +1,53 @@
 ---
-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.

 ---
 license: other
 task_categories:
 - image-segmentation
+language:
+- en
+pretty_name: GenSegDataset Processed Medical Segmentation Backup
 ---
+# GenSegDataset
+Processed and split medical image segmentation datasets used by the SegGen/NPJ experiments.
+Each `*.tar` expands to one dataset directory with the unified layout:
+```text
+<dataset>/<protocol>/<split>/{images,masks}/
+<dataset>/metadata.json
+<dataset>/manifest.jsonl
 ```
+Splits are already prepared; archives contain extracted PNG image/mask pairs, not raw compressed source zips.
+## Inventory
+| Dataset | Archive | Size on server | Total images across protocols | Protocols train/val/test |
+|---|---:|---:|---:|---|
+| `acdc_png` | `acdc_png.tar` | 61M | 726 | official:136/210/380 |
+| `bus_bra` | `bus_bra.tar` | 171M | 1875 | official:1312/188/375 |
+| `busi` | `busi.tar` | 17G | 4535 | fold01:545/78/157; fold02:545/78/157; fold03:546/78/156; fold04:547/78/155; fold05:547/78/155; synthonly:400/78/157 |
+| `consep` | `consep.tar` | 88M | 656 | official:352/80/224 |
+| `cvc_clinicdb` | `cvc_clinicdb.tar` | 938M | 612 | official:490/61/61 |
+| `cvc_colondb` | `cvc_colondb.tar` | 50M | 380 | official:266/38/76 |
+| `drive` | `drive.tar` | 1.8M | 20 | official:14/2/4 |
+| `endoscene` | `endoscene.tar` | 107M | 912 | official:547/183/182 |
+| `etis_larib` | `etis_larib.tar` | 30M | 196 | official:137/19/40 |
+| `fives` | `fives.tar` | 311M | 800 | official:480/120/200 |
+| `ham10000` | `ham10000.tar` | 1.7G | 10015 | holdout:7010/1002/2003 |
+| `idridd_segmentation` | `idridd_segmentation.tar` | 3.6M | 405 | fold01:43/11/27; fold02:43/11/27; fold03:43/11/27; fold04:43/11/27; fold05:44/10/27 |
+| `kvasir_seg` | `kvasir_seg.tar` | 17G | 1000 | official:800/100/100 |
+| `medsegdb_isic2018` | `medsegdb_isic2018.tar` | 24G | 5194 | holdout:2582/369/737; synthonly:400/369/737 |
+| `medsegdb_kits19` | `medsegdb_kits19.tar` | 1.3G | 20080 | fold01:2832/479/705; fold02:2966/231/819; fold03:3036/465/515; fold04:2854/286/876; fold05:2510/405/1101 |
+| `monuseg` | `monuseg.tar` | 411M | 816 | official:496/96/224 |
+| `pannuke_semantic` | `pannuke_semantic.tar` | 1.2G | 23703 | fold01:2722/2523/2656; fold02:2656/2722/2523; fold03:2523/2656/2722 |
+| `ph2` | `ph2.tar` | 27M | 200 | holdout:140/20/40 |
+| `refuge2` | `refuge2.tar` | 133M | 1200 | official:400/400/400 |
+| `udiat` | `udiat.tar` | 17M | 163 | official:114/16/33 |
+## Notes
+- The inventory file `HF_UPLOAD_INVENTORY.tsv` contains the same split/count audit in tabular form.
+- Image/mask stems were checked for alignment before upload.
+- Some archives are older backups; newly added archives were uploaded from `/home/wzhang/LSC/Dataset/Segmentation/processed_unified` on 2026-07-01.