| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - mri |
| - abdomen |
| - pancreas |
| - t1-weighted |
| - t2-weighted |
| - multi-center |
| - pansegdata |
| - pansegnet |
| pretty_name: PanSegData - Multi-center Abdominal MRI Pancreas Segmentation |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: t1_train |
| path: data/t1_train-* |
| - split: t1_test |
| path: data/t1_test-* |
| - split: t2_train |
| path: data/t2_train-* |
| - split: t2_test |
| path: data/t2_test-* |
| dataset_info: |
| features: |
| - name: case_id |
| dtype: string |
| - name: modality |
| dtype: string |
| - name: center |
| dtype: string |
| - name: center_name |
| dtype: string |
| - name: split |
| dtype: string |
| - name: patient_uid |
| dtype: string |
| - name: paired_case_id |
| dtype: string |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: overlay |
| dtype: image |
| - name: slice_index |
| dtype: int32 |
| - name: slice_selection |
| dtype: string |
| - name: n_slices |
| dtype: int32 |
| - name: shape_xyz |
| dtype: string |
| - name: spacing_xyz |
| dtype: string |
| - name: slice_thickness_mm |
| dtype: float32 |
| - name: axcodes_image |
| dtype: string |
| - name: axcodes_mask |
| dtype: string |
| - name: mask_affine_unreliable |
| dtype: bool |
| - name: fg_voxels |
| dtype: int64 |
| - name: fg_fraction |
| dtype: float64 |
| - name: n_fg_slices |
| dtype: int32 |
| - name: fg_slice_fraction |
| dtype: float32 |
| - name: gender |
| dtype: string |
| - name: age |
| dtype: int32 |
| - name: mri_brand |
| dtype: string |
| - name: magnet_strength_t |
| dtype: float32 |
| splits: |
| - name: t1_train |
| num_bytes: 30193255 |
| num_examples: 313 |
| - name: t1_test |
| num_bytes: 10035546 |
| num_examples: 72 |
| - name: t2_train |
| num_bytes: 32647077 |
| num_examples: 312 |
| - name: t2_test |
| num_bytes: 11753532 |
| num_examples: 70 |
| download_size: 84568706 |
| dataset_size: 84629410 |
| --- |
| |
| # PanSegData — Multi-center Abdominal MRI Pancreas Segmentation |
|
|
| **767 abdominal MRI volumes (385 T1W + 382 T2W) with manual pancreas masks**, collected |
| across **five US institutions** between March 2004 and November 2022 and released by the |
| Machine & Hybrid Intelligence Lab at Northwestern University. This is the MRI half of the |
| PanSegNet study, and it is one of the very few large, multi-center, *manually annotated* |
| pancreas MRI resources in existence. |
|
|
| The pancreas is among the hardest abdominal organs to segment — small, soft-tissue-isointense, |
| and highly variable in shape and position. Here the foreground is on average **0.44 % of voxels**. |
|
|
| ## ⚠️ What this mirror contains — read first |
|
|
| > **MRI only. There is no CT here.** The paper is titled *"Large-scale multi-center **CT and |
| > MRI** segmentation of pancreas"*, but only the MRI is the authors' own releasable data. |
| > The paper's 1,350 CT scans are third-party public datasets (AbdomenCT-1K 1000, AMOS 200, |
| > WORD 120, BTCV 30) used only for benchmarking — they are not part of PanSegData and are not |
| > redistributed here. If you expected 2,117 volumes, you get **767**. |
|
|
| > **Do not join `t1` and `t2` by filename.** The two modalities were renumbered |
| > *independently*, so the same released id means **different patients** in 259 of the 370 |
| > ids that exist in both. Use the `patient_uid` column. See |
| > [Cross-modality identity](#-cross-modality-identity-the-single-biggest-trap). |
| |
| > **Do not reorient the masks by their own affine.** 66 of the 385 T1 masks carry a |
| > sign-flipped direction matrix. See [Mask affine defect](#-mask-affine-defect-66-t1-cases). |
| |
| ## Dataset Details |
| |
| | Field | Value | |
| |---|---| |
| | Modality | Abdominal MRI — **T1-weighted (385)** and **T2-weighted (382)** | |
| | Body part | Abdomen — **pancreas**, single foreground class | |
| | Labels | `0` background · `1` pancreas (verified: the only values present in all 767 masks) | |
| | Patients | **405 distinct**, of whom **362 were imaged in both modalities** | |
| | Centers | 5 — NYU, Mayo Clinic Florida, Northwestern, Allegheny Health Network, Mayo Clinic Arizona | |
| | Acquisition window | March 2004 – November 2022 | |
| | Format | `.nii.gz` (NIfTI-1), nnU-Net v1 raw layout upstream | |
| | Geometry | **not resampled, not cropped, not co-registered** — native per-case | |
| | In-plane | 0.53 – 1.80 mm · **166 distinct shapes (T1)**, 165 (T2) | |
| | Slice thickness | **1.8 – 10.4 mm** — strongly anisotropic, typical 2D-acquired abdominal MRI | |
| | Slices per volume | 20 – 148 (T1), 20 – 102 (T2) | |
| | Foreground | **0.44 % of voxels**; present on only **~43.5 % of slices** | |
| | Empty masks | **none** — every one of the 767 volumes has annotated pancreas | |
| | License | **CC BY-NC 4.0** — redistribution explicitly permitted, non-commercial only | |
| | Paper | Zhang et al., *Medical Image Analysis* **99**:103382 (2025) · doi:10.1016/j.media.2024.103382 | |
| | Official source | https://osf.io/kysnj/ (author-controlled; md5s verified against the OSF API) | |
| |
| ### Per-center composition (matches paper Table 2 cell-for-cell) |
| |
| | Center | Code | T1W | T2W | Split | |
| |---|---|---|---|---| |
| | NYU Medical Center | `NYU` | 162 | 162 | train | |
| | Mayo Clinic Florida | `MCF` | 151 | 150 | train | |
| | Northwestern University | `NWU` | 30 | 19 | test | |
| | Allegheny Health Network | `AHN` | 17 | 28 | test | |
| | Mayo Clinic Arizona | `MCA` | 25 | 23 | test | |
| | **Total** | | **385** | **382** | | |
| |
| ## Splits — center-based, and why it has to be |
| |
| The official release has **no split file**: all 767 volumes ship under `imagesTr/` + `labelsTr/`. |
| This mirror materialises the **paper's own protocol** — centers 1–2 (NYU, Mayo FL) as the |
| internal cohort, centers 3–5 (NWU, AHN, MCA) held out as the external test set: |
| |
| | Config | train | test | total | |
| |---|---|---|---| |
| | `t1` | 313 | 72 | 385 | |
| | `t2` | 312 | 70 | 382 | |
| |
| This is not merely convention. **Center is a property of the patient**, so a center-based |
| split is automatically *patient-disjoint across modalities as well*. A random split would put |
| some of the 362 dual-modality patients in `t1/train` and their other scan in `t2/test`. The |
| `center` column is preserved so you can re-derive any other split, including the paper's |
| 5-fold CV over the internal cohort. |
| |
| ## ⚠️ Cross-modality identity — the single biggest trap |
| |
| The released ids (`NYU_0001`, `MCF_0042`, …) were assigned **independently per modality**. |
| Each excluded scan shifts every subsequent number, and the two modalities excluded different |
| scans (64 from T1, 58 from T2). Measured on this mirror: |
| |
| | | | |
| |---|---| |
| | released ids present in both `t1` and `t2` | **370** | |
| | …that refer to the **same** patient | **111** | |
| | …that refer to a **different** patient | **259** | |
| |
| Mismatches by center: NYU 158, MCF 48, NWU 19, AHN 17, MCA 17. |
| Example: `AHN_0001` is original `AHN05` in T1 but `ahn_02` in T2. |
| |
| **Use `patient_uid`.** It is derived from the authors' official `T1-name_mapping.json` / |
| `T2-name_mapping.json` by folding case, separators and leading zeros |
| (`AHN05` and `ahn_05` → `ahn5`). Verified globally unique: **no original id is used by more |
| than one center**, and within a modality every patient contributes exactly one scan. |
|
|
| > A note for anyone comparing against other write-ups: **AHN *is* joinable across |
| > modalities.** T1 spells it `AHN05` and T2 spells it `ahn_05`; that is a formatting |
| > difference, not a missing key. All 17 T1 AHN patients are present in T2. Patients imaged |
| > in both modalities, by center: NYU 161, MCF 150, NWU 19, AHN 17, MCA 15 = **362**. |
| |
| `metadata/patient_crossref.csv` gives the full `patient_uid ↔ (t1 id, t2 id)` table. |
| |
| ### Unreconciled count |
| |
| The paper states *"767 scans from 499 adult participants"*. Tracing all 767 released scans |
| back through the official mapping files yields **405** distinct original patient ids, not 499. |
| The mapping files list 889 original scans of which 122 are marked `"Not included"`. The most |
| plausible reading is that **499 counts the full collected cohort while 405 is what was actually |
| released**, but the paper attributes 499 directly to the 767, so this is flagged rather than |
| silently resolved. Nothing downstream depends on it; `patient_uid` is derived from the released |
| scans only. |
|
|
| ## ⚠️ Mask affine defect — 66 T1 cases |
|
|
| **In 66 of 385 T1 cases the mask's affine disagrees with the image's.** The images are |
| `LPS` while the masks read `RAS` (28 cases) or `RAI` (38 cases) — all of them in the MCF |
| center. T2 is unaffected (0 cases). |
|
|
| The affines differ **only in the sign of the diagonal**, with identical zooms and identical |
| array shapes: |
|
|
| ``` |
| MCF_0001 image affine diag = [-1.484, -1.484, 7.7] zooms (1.484, 1.484, 7.7) |
| mask affine diag = [+1.484, +1.484, 7.7] zooms (1.484, 1.484, 7.7) |
| ``` |
|
|
| That is the signature of a mask written without direction cosines, **not** of genuinely |
| reordered voxel data. Measured here to confirm it, using the fact that MRI background outside |
| the body is ≈0 — a correctly aligned pancreas mask must not sit on air: |
|
|
| | Alignment | mean fraction of mask voxels below the body/air threshold | |
| |---|---| |
| | **Raw voxel grid** (ignore the mask affine) | **0.109** ✅ | |
| | Reorient each by its own affine | 0.312 ❌ | |
| | *Control: concordant cases, raw* | *0.248 mean / 0.085 median* | |
|
|
| Raw voxel alignment wins in **64 of 66** cases. (The 2 exceptions, `MCF_0052` and `MCF_0107`, |
| score >0.69 under *both* alignments — the heuristic simply fails on them; the control's p90 of |
| 0.85 shows such values occur in perfectly concordant cases too. They are not counter-evidence.) |
|
|
| > **What to do:** take geometry from the **image**, and apply the **same** transform to the |
| > mask. Never call `nib.as_closest_canonical()` — or any reorientation — on the mask |
| > independently. Doing so is the natural way to handle this dataset's mixed `LPI`/`LPS`/`RAS` |
| > orientations, and it silently corrupts **17 % of the T1 set**. |
|
|
| The headers are **deliberately not patched**, so this mirror stays byte-identical to the |
| official release. The affected cases are flagged by `mask_affine_unreliable: true` in the jsonl. |
|
|
| ## Other geometry gotchas |
|
|
| - **Mixed orientation across cases**: T1 is `LPI` 223 / `LPS` 143 / `RAS` 19; T2 is `LPI` 251 / |
| `LPS` 131. Reorient to a canonical frame *using the image affine* or L/R and S/I flip |
| silently between cases. |
| - **Mixed image dtype**: T1 `float32` 330, `int16` 35, `uint16` 19, `float64` 1; |
| T2 `int16` 362, `uint16` 19, `float32` 1. Masks are `uint16` (766) / `uint8` (1). |
| Do not assume a dtype; do not assume a fixed intensity range (no upstream normalisation). |
| - **Sparse foreground**: 0.44 % of voxels, and only ~43.5 % of slices carry any pancreas |
| (min 10.8 %). A slice sampler with a small budget can easily draw only empty slices. |
| - **Anisotropy**: through-plane spacing is 1.8–10.4 mm against 0.53–1.80 mm in-plane. Any |
| 3D model or physical-unit metric must read real spacing from the header. |
|
|
| ## Ground truth — single tier, 100 % manual |
|
|
| > *"Five radiologists (one per center) manually segmented the pancreas on axial T1W and T2W |
| > MRI scans using ITK-SNAP. A senior radiologist double-checked the annotations for quality |
| > and consistency."* — paper §3.3 |
|
|
| There is exactly **one mask per volume** and no alternative rater tier is distributed. **No |
| AI-assisted, semi-automatic, or pseudo-labelled masks anywhere** — PanSegNet is trained *on* |
| these labels and was never used to produce them. Annotation took ≈25 min per scan, following |
| a protocol agreed among the radiologists beforehand. |
|
|
| **Human ceiling** (the authors' own sub-studies; the repeat annotations are *not* released): |
|
|
| | | T1W | T2W | |
| |---|---|---| |
| | Inter-observer Dice (50 scans) | 0.8014 | 0.8058 | |
| | Inter-observer Cohen's κ | 0.624 | 0.638 | |
| | Intra-observer Dice (20 scans, 4-week washout) | 0.960 | 0.936 | |
|
|
| Inter-observer agreement is only *moderate* by the authors' own description — a model at |
| Dice ≈0.80 is already at the human agreement ceiling. |
|
|
| ## ✅ Cross-dataset overlap: none |
|
|
| Unusually clean for a pancreas dataset. The 767 MRI volumes are **newly collected, |
| IRB-approved private hospital data**, not curated from any public archive. They therefore |
| **cannot** overlap NIH Pancreas-CT, MSD Task07_Pancreas, AbdomenCT-1K, FLARE22/23, AMOS, |
| WORD, BTCV, or PANORAMA — every one of those is CT, and the AMOS MRI subset is a disjoint |
| public cohort. |
| |
| The overlap risk in the *paper* lives entirely in the CT half, which is not shipped here: |
| AbdomenCT-1K is itself curated from 12 centers **including NIH and MSD**, so it transitively |
| contains both NIH Pancreas-CT and MSD Task07_Pancreas. Keep that in mind if you ever pair |
| this MRI set with those CT benchmarks. |
|
|
| ## Structure |
|
|
| ``` |
| t1/train/images/NYU_0001_0000.nii.gz # 313 T1W volumes (NYU + MCF) |
| t1/train/masks/NYU_0001.nii.gz # 313 masks, same voxel grid |
| t1/test/images/ t1/test/masks/ # 72 T1W volumes (NWU + AHN + MCA) |
| t2/train/... t2/test/... # 312 / 70 T2W volumes |
| |
| t1_train.jsonl t1_test.jsonl # per-case metadata |
| t2_train.jsonl t2_test.jsonl |
| |
| metadata/T1-name_mapping.json # official, verbatim from OSF |
| metadata/T2-name_mapping.json |
| metadata/t2_info_osf.xlsx # official T2 demographics (408 rows) |
| metadata/patient_crossref.csv # patient_uid <-> t1 id <-> t2 id (derived here) |
| metadata/demographics.csv # xlsx joined onto released ids via patient_uid |
| |
| data/*.parquet # display-only preview, see below |
| README.md LICENSE.txt |
| ``` |
|
|
| Filenames are the **official** ones, including the nnU-Net `_0000` channel suffix on images. |
|
|
| ### jsonl columns |
|
|
| | Column | Meaning | |
| |---|---| |
| | `case_id` | released id, e.g. `"NYU_0001"` | |
| | `center`, `center_name` | `"NYU"` … / full institution name | |
| | `modality` | `"T1W"` or `"T2W"` | |
| | `image`, `mask` | repo-relative paths | |
| | `split` | `"train"` (NYU+MCF) or `"test"` (NWU+AHN+MCA) | |
| | **`patient_uid`** | **cross-modality patient key — group on this, never on `case_id`** | |
| | `original_name` | the pre-anonymisation filename from the official mapping | |
| | `paired_case_id` | the same patient's id in the *other* modality, or `null` | |
| | `has_other_modality` | whether this patient also appears in the other config | |
| | `n_slices`, `shape_xyz` | geometry | |
| | `spacing_xyz`, `slice_thickness_mm` | **real spacing from the header** | |
| | `axcodes_image`, `axcodes_mask` | orientation as declared by each file | |
| | **`mask_affine_unreliable`** | **`true` for the 66 sign-flipped T1 masks** | |
| | `image_dtype`, `mask_dtype` | not constant — see gotchas | |
| | `intensity_min`, `intensity_max` | per case, never renormalised | |
| | `label_values` | always `[0, 1]` | |
| | `fg_voxels`, `fg_fraction` | pancreas volume in voxels / fraction | |
| | `n_fg_slices`, `fg_slice_fraction` | how many slices carry pancreas | |
| | `demographics` | `{gender, age, mri_brand, magnet_strength}` where known, else `null` | |
|
|
| ### The parquet preview layer is display-only |
|
|
| `data/*.parquet` exists so the HF Dataset Viewer can render this dataset. Each row holds one |
| slice of one volume as PNG: `image` (grayscale, percentile-windowed), `mask`, and `overlay`. |
|
|
| > **The preview renders the slice with the LARGEST pancreas area, not the middle slice.** |
| > With foreground on only ~43.5 % of slices, a middle-slice preview would show an empty mask |
| > for over half the dataset. `slice_index` records which slice was rendered. |
| |
| > **Do not train or evaluate on the preview.** Its intensities are percentile-windowed to |
| > 8-bit for display and it holds one slice per volume. The real data is the byte-identical |
| > `.nii.gz` at the repo root. |
| |
| ## Source & Citation |
| |
| - Official data: https://osf.io/kysnj/ — CC BY-NC 4.0, anonymous download, no registration. |
| - Code (PanSegNet): https://github.com/NUBagciLab/PaNSegNet |
| - The medsam-datasetlist entry records the license as "GPL 3.0"; that is **wrong** — see |
| `LICENSE.txt` in this repo for the evidence. |
| |
| ```bibtex |
| @article{zhang2025pansegnet, |
| author = {Zhang, Zheyuan and Keles, Elif and Durak, Gorkem and Taktak, Yavuz and |
| Susladkar, Onkar and Gorade, Vandan and Jha, Debesh and |
| Ormeci, Asli C. and Medetalibeyoglu, Alpaslan and Yao, Lanhong and |
| Wang, Bin and Isler, Ilkin Sevgi and Peng, Linkai and Pan, Hongyi and |
| Vendrami, Camila L. and Bourhani, Amir and Velichko, Yury and |
| Gong, Boqing and Spampinato, Concetto and Pyrros, Ayis and |
| Tiwari, Pallavi and Klatte, Derk C. F. and Engels, Megan and |
| Hoogenboom, Sanne and Bolan, Candice W. and Agarunov, Emil and |
| Harfouch, Nassier and Huang, Chenchan and Bruno, Marco J. and |
| Schoots, Ivo and Keswani, Rajesh N. and Miller, Frank H. and |
| Gonda, Tamas and Yazici, Cemal and Tirkes, Temel and |
| Turkbey, Baris and Wallace, Michael B. and Bagci, Ulas}, |
| title = {Large-scale multi-center CT and MRI segmentation of pancreas with deep learning}, |
| journal = {Medical Image Analysis}, |
| volume = {99}, |
| pages = {103382}, |
| year = {2025}, |
| doi = {10.1016/j.media.2024.103382} |
| } |
| ``` |
| |
