--- license: other language: - en pretty_name: MedSeg-7D size_categories: - 10K **Why this exists.** Many existing medical-augmentation papers report > non-comparable numbers because each uses a different (often undocumented) > train/test split, and several datasets have hidden leakage if split at > the image level (CVC same-video frames, ACDC same-patient slices, BraTS > same-volume slices). This release fixes one canonical split per dataset > so future work can be paired-comparable. --- ## Contents ``` MedSeg-7D/ ├── README.md ├── ACDC/ (cardiac MRI, 100 patients) │ ├── images/ 2D slices: patient_frame_slice_.png │ ├── masks/ matching 2D-slice mask filenames (any-structure binary) │ ├── 3D/ ORIGINAL 3D NIfTI volumes (challenge layout) │ │ ├── training/ │ │ │ ├── patient001/ Info.cfg + patient001_4d.nii.gz + frame01.nii.gz + frame01_gt.nii.gz + frame12.nii.gz + frame12_gt.nii.gz │ │ │ └── ... (100 patients) │ │ └── testing/ 50 held-out patients (challenge test set) │ └── split_info.json CANONICAL patient-level split (seed=42, 80/20) │ # 2D and 3D share the same 100-patient training cohort. The 3D side │ # additionally ships the official 50 challenge test patients, which the │ # 2D side does NOT include (we re-split the 100 train patients 80/20). │ ├── BraTS2020/ (brain MRI FLAIR, 369 volumes → 22677 slices) │ ├── images/ │ │ ├── volume_1/ volume_1_slice_.png (FLAIR channel, ~50-80 slices/vol) │ │ ├── volume_2/ │ │ └── ... (369 vols) │ ├── masks/ │ │ ├── volume_1/ matching whole-tumor binary mask │ │ └── ... (369 vols) │ └── split_info.json CANONICAL volume-level split (seed=42, 295/74) │ # NOTE: BraTS slices are nested into per-volume subdirectories because of │ # HuggingFace's 10000 files-per-directory limit. Filenames preserve the │ # original volume_X_slice_Y.png convention. │ ├── BraTS2021_3D/ (brain MRI 3D NIfTI, 1251 patients — superset of BraTS2020) │ ├── BraTS2021_00000/ 5 NIfTI files: t1, t1ce, t2, flair, seg (4 modalities + GT) │ ├── BraTS2021_00002/ │ └── ... (1251 patient dirs) │ # IMPORTANT: This is BraTS *2021*, a SUPERSET of BraTS 2020. The 369 │ # volumes in our 2D `BraTS2020/` are a subset of the 1251 here. Patient │ # IDs differ between the 2020 and 2021 releases, so split_info.json │ # (volume-level for 2020) does NOT apply to BraTS2021_3D directly. Do │ # not mix 2D and 3D Dice numbers. │ ├── BUSI/ (breast ultrasound, 780 images) │ ├── images/ │ └── masks/ masks suffixed _mask.png │ ├── CVC-ClinicDB/ (endoscopy polyp, 612 frames / 29 video sequences) │ ├── PNG/ │ │ ├── Original/ RGB frames │ │ └── Ground Truth/ binary masks │ ├── TIF/ original release format │ ├── metadata.csv per-frame metadata │ ├── class_dict.csv │ ├── pranet_split.json PRIMARY: PraNet 550/62 image-level split (literature-standard) │ └── video_split_seed42.json ALTERNATIVE: leakage-free 23/6 video-level split (more rigorous) │ ├── Kvasir-SEG/ (endoscopy polyp, 1000 images) │ ├── images/ RGB frames │ ├── masks/ binary masks │ ├── bbox/ bounding boxes (auxiliary) │ ├── pranet_split.json PRIMARY: PraNet 900/100 train/test split (literature-standard) │ └── kavsir_seg_README.md original release notes │ ├── REFUGE2/ (fundus optic disc, 1200 images = 400 train + 400 val + 400 test) │ ├── train/ {images/, mask/} 400 images │ ├── val/ {images/, mask/} 400 images │ └── test/ {images/, mask/} 400 images │ └── ISIC2018/ (dermoscopy lesions, 2594 train + 100 val + 1000 test) ├── train/ {images/, masks/} ├── validation/ {images/, masks/} └── test/ {images/, masks/} ``` Approximate total size: ~18 GB. --- ## Per-dataset details ### 1. ACDC — Cardiac cine-MRI | | | |---|---| | **Modality** | Cardiac cine-MRI (2D slices) | | **Original task** | Multi-class cardiac structure segmentation | | **Patients / slices** | 100 / 1841 | | **Mask classes** | 4 (background, RV, myocardium, LV) — preserved as in the original release | | **Split type (canonical)** | **Patient-level**, 80 train / 20 test, seed=42 | | **Split file** | `ACDC/split_info.json` | | **Leakage risk** | None at patient level. Slice-level random split would leak adjacent slices and inflate Dice ~5 points. | | **Source** | [ACDC Challenge (MICCAI 2017)](https://www.creatis.insa-lyon.fr/Challenge/acdc/) | | **Reference** | Bernard et al., *IEEE TMI 2018* | | **License** | Original ACDC license; please refer to the original challenge website. | ### 2. BraTS 2020 — Brain tumor MRI (FLAIR slices) | | | |---|---| | **Modality** | Brain MRI, FLAIR channel | | **Original task** | Multi-class tumor segmentation | | **Volumes / slices** | 369 / 22677 (this release: FLAIR-only 2D slices) | | **Mask convention here** | Whole-tumor binary (label 1+2+4 → 1) | | **Split type (canonical)** | **Volume-level**, 295 train / 74 test, seed=42 | | **Split file** | `BraTS2020/split_info.json` | | **Leakage risk** | None at volume level. Slice-level random would leak adjacent slices ~5 Dice points. | | **Note** | Only the FLAIR modality is included. The original BraTS release has T1/T1ce/T2 in addition. If you need multi-modal data, fetch the original release. | | **Source** | [BraTS 2020 Challenge](https://www.med.upenn.edu/cbica/brats2020/) | | **Reference** | Menze et al., *IEEE TMI 2015*; Bakas et al., 2017 | | **License** | Original BraTS license; please refer to the challenge website. | ### 3. BUSI — Breast ultrasound | | | |---|---| | **Modality** | B-mode breast ultrasound | | **Original task** | Lesion segmentation (benign / malignant / normal classes are also available) | | **Images** | 780 | | **Mask convention** | Binary foreground; mask filenames carry `_mask.png` suffix | | **Split type (canonical)** | Image-level, 80/20, seed=42 | | **Leakage risk** | ⚠️ The release does **not** publish patient IDs. Multiple images may come from the same patient. The image-level split is the community standard; "patient-level" cannot be verified from the release. | | **Source** | [BUSI Dataset (Cairo University)](https://scholar.cu.edu.eg/?q=afahmy/pages/dataset) | | **Reference** | Al-Dhabyani et al., *Data in Brief 2020* | | **License** | CC-BY-4.0 | ### 4. CVC-ClinicDB — Colonoscopy polyp | | | |---|---| | **Modality** | Colonoscopy (RGB endoscopy) | | **Original task** | Polyp segmentation | | **Frames / video sequences** | 612 / 29 | | **Mask convention** | Binary polyp foreground | | **PRIMARY split (literature-standard)** | PraNet's **550/62** image-level train/test, used by PraNet, Polyp-PVT, SANet, ESFPNet and most polyp papers | | **Primary split file** | `CVC-ClinicDB/pranet_split.json` | | **ALTERNATIVE split (leakage-free)** | **Video-level**, 23 train / 6 test sequences, seed=42 (489 frames train, 123 frames test) | | **Alternative split file** | `CVC-ClinicDB/video_split_seed42.json` | | **Important note** | The PraNet split is image-level and **leaks same-video frames** across train/test (CVC has 29 underlying sequences). Use it for direct comparison to literature; use video-level for honest leakage-free generalization numbers. The two are not directly cross-comparable in absolute Dice. | | **Source** | [CVC-ClinicDB](https://polyp.grand-challenge.org/CVCClinicDB/) | | **Reference** | Bernal et al., *Computerized Medical Imaging and Graphics 2015* | | **License** | Released for academic use; cite the original paper. | ### 5. Kvasir-SEG — Colonoscopy polyp | | | |---|---| | **Modality** | Colonoscopy (RGB endoscopy) | | **Original task** | Polyp segmentation | | **Images** | 1000 | | **Mask convention** | Binary polyp foreground | | **PRIMARY split (literature-standard)** | PraNet's **900/100** train/test (specific file lists), used by PraNet, Polyp-PVT, SANet, ESFPNet and the entire polyp-segmentation literature | | **Primary split file** | `Kvasir-SEG/pranet_split.json` | | **Leakage risk** | The release does not publish per-procedure metadata. Image-level is the community standard; per-procedure leakage cannot be audited. | | **Note** | Filenames in our release use `.jpg` (the original Kvasir-SEG extension); PraNet ships them as `.png` after conversion — basenames match exactly. Auxiliary `bbox/` (bounding boxes) included from the original release. | | **Source** | [Kvasir-SEG](https://datasets.simula.no/kvasir-seg/) | | **Reference** | Jha et al., *MMM 2020* | | **License** | CC-BY-4.0 | ### 6. REFUGE2 — Fundus optic disc | | | |---|---| | **Modality** | Fundus photography | | **Original task** | Optic disc and cup segmentation | | **Images** | 1200 = 400 train + 400 validation + 400 test (full official challenge release) | | **Mask convention** | Multi-class (BG / disc / cup) preserved; for binary disc segmentation, treat any non-background pixel as foreground | | **Split type** | Pre-released **400/400/400 train/val/test** split is preserved | | **Leakage risk** | None — each image is from a different patient by protocol. | | **Caveat** | Modern segmenters reach ≥99.9 Dice on optic-disc segmentation; this dataset is **saturated** for that task. Use only when you specifically need fundus / glaucoma data. | | **Source** | [REFUGE2 Challenge](https://refuge.grand-challenge.org/) | | **Reference** | Orlando et al., *Medical Image Analysis 2020*; Fang et al., *Medical Image Analysis 2022* | | **License** | Original REFUGE2 license; please refer to the challenge website. | ### 7. ISIC 2018 — Dermoscopy | | | |---|---| | **Modality** | Dermoscopy | | **Original task** | Skin lesion segmentation (Task 1) | | **Images** | 2594 train + 100 val + 1000 test (this release: PNG-extracted from the original ISIC 2018 archive) | | **Mask convention** | Binary lesion foreground (any-pixel > 0 → 1) | | **Split type** | Pre-released train/validation/test split is preserved | | **Leakage risk** | The release does not publish patient IDs. Multiple lesions per patient are possible but cross-lesion contamination is generally considered low risk. | | **Source** | [ISIC 2018 Challenge](https://challenge.isic-archive.com/landing/2018) | | **Reference** | Codella et al., 2019; Tschandl et al., *Sci. Data 2018* | | **License** | CC-BY-NC-4.0 (HAM10000-derived images) | --- ## Comparison to literature and existing HuggingFace cards We audited the most common split conventions in published segmentation papers (MICCAI / IEEE TMI / MIA / CVPR / NeurIPS) and the two existing HuggingFace community cards for the same datasets, then aligned our defaults where sensible. Summary: | Dataset | Mainstream literature default | HuggingFace community card | **Our default** | Verdict | |---|---|---|---|---| | CVC-ClinicDB | PraNet's **550/62** image-level files (de facto standard since 2020) | `Angelou0516/CVC-ClinicDB`: 80/10/10 image-level, ESFPNet split | **PraNet 550/62 (`pranet_split.json`) as primary; video-level 23/6 (`video_split_seed42.json`) as leakage-free alternative** | ✅ Matches PraNet exactly + adds a leakage-audit option that nobody else ships | | Kvasir-SEG | PraNet's **900/100** file list (de facto standard) | `kowndinya23/Kvasir-SEG`: 880/120 (no test) | **PraNet 900/100 (`pranet_split.json`)** | ✅ Matches PraNet exactly | | BUSI | Image-level random; growing minority does 5-fold + de-duplication (BUS-Set, Med Phys 2023, documents duplicate leakage) | n/a | Image-level 80/20 seed=42 | Matches majority; **flag**: BUSI release has documented duplicates, and patient IDs are not public, so true patient-level splits are not possible | | ISIC 2018 | Official 2594/100/1000 OR pooled 80/20 | varies | Official 2594/100/1000 preserved | Matches official challenge split | | REFUGE2 | Official 400/400/400 (train/val/test domain-shift design) | varies | Official train/val/test preserved | Matches official | | ACDC | Patient-level; TransUNet 70/10/20 of the 100 train OR nnU-Net 5-fold patient CV | rarely correct on HF | Patient-level 80/20 seed=42 (in `split_info.json`); 3D side **also** ships official 100-train + 50-test challenge layout | Stricter than the careless cards; consistent with TransUNet/nnU-Net practice | | BraTS 2020 | Volume-level; nnU-Net 5-fold patient CV is the most-cited recipe | rarely correct on HF | Volume-level 80/20 seed=42 (295/74) | Matches the careful camp; nnU-Net's 5-fold is a reasonable alternative on the same volumes | **Mainstream papers we cross-checked**: PraNet (Fan et al., MICCAI 2020), Polyp-PVT (Dong et al., 2021), ESFPNet (Chang et al., 2024), BUS-Set (Thomas et al., Med Phys 2023), TransUNet (Chen et al., 2021), SwinUNet (Cao et al., 2022), nnU-Net (Isensee et al., Nat. Methods 2021). ### Notable disagreements with HuggingFace community cards - `kowndinya23/Kvasir-SEG` (880/120) merges the test fold into validation, making it **non-comparable to PraNet's 900/100**. Ours preserves test/val separation. - `Angelou0516/CVC-ClinicDB` does image-level 80/10/10 without flagging the same-video frame leakage that affects all 3 splits. We add an explicit video-level split for leakage-free evaluation. - Neither HuggingFace card we found exposes patient-level splits for ACDC or BraTS — we provide them via `split_info.json`. ### When to *not* use our defaults - If you must **directly compare to PraNet/Polyp-PVT** numbers, use their released 1450/test files (not in this bundle, but reproducible from the raw images here). - If you need **nnU-Net 5-fold CV** on ACDC or BraTS, regenerate folds with the standard nnU-Net recipe — our 80/20 split is a single-fold approximation. - If you need **BraTS 2021 (1251 volumes)** instead of 2020 (369), the 3D version is shipped under `BraTS2021_3D/` (subset of 2020 patients is included; new 2021-specific patients are added). --- ## Recommended use **For paired-comparison evaluation across methods**, lock to the canonical splits in this release: ```python import json, os from huggingface_hub import snapshot_download ROOT = snapshot_download("MaybeRichard/MedSeg-7D", repo_type="dataset") # ACDC (patient-level) info = json.load(open(os.path.join(ROOT, "ACDC", "split_info.json"))) train_patients = set(info["train_patients"]) # enumerate slices, check patient ID in filename to assign train/test # BraTS (volume-level) — slices are nested under per-volume subdirs info = json.load(open(os.path.join(ROOT, "BraTS2020", "split_info.json"))) train_volumes = set(info["train_patients"]) # key name retained from original # To enumerate all training slices: # for vol in train_volumes: # for img_path in glob.glob(f"{ROOT}/BraTS2020/images/{vol}/*.png"): # ... # Kvasir-SEG (PraNet 900/100, literature standard) info = json.load(open(os.path.join(ROOT, "Kvasir-SEG", "pranet_split.json"))) train_files = set(info["train_files"]) # 900 file basenames (.jpg) test_files = set(info["test_files"]) # 100 file basenames (.jpg) # CVC-ClinicDB (PraNet 550/62, literature standard — has same-video leakage!) info = json.load(open(os.path.join(ROOT, "CVC-ClinicDB", "pranet_split.json"))) train_files = set(info["train_files"]) # 550 frames as .png test_files = set(info["test_files"]) # 62 frames as .png # CVC-ClinicDB (video-level 23/6, leakage-free alternative) info = json.load(open(os.path.join(ROOT, "CVC-ClinicDB", "video_split_seed42.json"))) train_seqs = set(info["train_sequences"]) ``` For the **3D NIfTI versions**: ```python import nibabel as nib import os # ACDC 3D (original challenge layout, 100 train + 50 test patients) patient_dir = os.path.join(ROOT, "ACDC", "3D", "training", "patient001") img = nib.load(os.path.join(patient_dir, "patient001_frame01.nii.gz")).get_fdata() gt = nib.load(os.path.join(patient_dir, "patient001_frame01_gt.nii.gz")).get_fdata() # img shape: (H, W, num_short_axis_slices); gt has 4 classes (0=BG, 1=RV, 2=Myo, 3=LV) # BraTS 2021 3D (1251 patients, 4 modalities + GT each) pat = os.path.join(ROOT, "BraTS2021_3D", "BraTS2021_00000") flair = nib.load(os.path.join(pat, "BraTS2021_00000_flair.nii.gz")).get_fdata() seg = nib.load(os.path.join(pat, "BraTS2021_00000_seg.nii.gz")).get_fdata() # seg has 4 classes (0=BG, 1=necrotic, 2=edema, 4=enhancing); whole-tumor = (seg > 0) ``` For **BUSI**, the only dataset without a packaged split file, use a seed-fixed image-level 80/20 split: ```python import numpy as np def get_image_level_split(n_images, seed=42, train_ratio=0.8): perm = np.random.RandomState(seed).permutation(n_images) n_train = int(n_images * train_ratio) return perm[:n_train], perm[n_train:] ``` (BUSI's release does not include patient IDs, so a true patient-level split is not possible. See per-dataset notes for caveats.) --- ## Known caveats and good practices 1. **Never use slice-level random split for ACDC or BraTS.** Same-patient adjacent slices end up on both sides and inflate Dice ~5 points. Always read `split_info.json`. 2. **CVC image-level split is leaky.** Same-video frames cross train/test. Use the video-level split (`video_split_seed42.json`) for clean evaluation. Use image-level only for direct comparison to legacy literature, and label such results as "leakage-audited / auxiliary". 3. **BUSI / Kvasir / ISIC do not provide patient IDs.** Image-level random is the de-facto community standard; do not claim "patient-level independent" — there is no metadata to verify it. 4. **REFUGE2 saturates at ~99.9 Dice.** Don't use it as a downstream evaluator for augmentation studies; use it only when you need a fundus / optic-disc task specifically. 5. **Mask conventions vary across datasets.** Some are multi-class (ACDC: 4 classes; BraTS original: 4 classes; REFUGE2: 3 classes). For binary segmentation, use `mask > 0`. The released masks here keep the original multi-class labels where applicable (no information lost), so users can choose to binarize as needed. 6. **All images and masks are at original resolution.** No pre-processing baked in; you can resize per your protocol. --- ## Citation If this release is useful, please cite both the original dataset papers (see per-dataset references above) and the evaluation-protocol audit that produced these canonical splits: ```bibtex @inproceedings{medseg7d2026, title = {An Evaluation-Protocol Audit of Pixel- vs.\ Latent-Space Diffusion Augmentation for Medical Image Segmentation}, author = {Anonymous}, booktitle = {NeurIPS 2026 (E\&D Track)}, year = {2026} } ``` ## License This release does **not** redistribute datasets that are not already publicly available. Each dataset retains its original license; consult each per-dataset section above. The split metadata files (`split_info.json`, `video_split_seed42.json`) are released under MIT.