| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - histopathology |
| - nuclei |
| - h-and-e |
| - monuseg |
| pretty_name: MoNuSeg |
| --- |
| |
| # MoNuSeg (Multi-Organ Nucleus Segmentation) |
|
|
| H&E-stained histopathology images (from TCGA WSIs at 40x magnification) |
| with per-nucleus binary segmentation masks. MICCAI 2018 challenge. |
|
|
| ## Overview |
| - **Modality:** H&E histopathology (brightfield microscopy) |
| - **Image size:** 1000x1000 RGB |
| - **Samples:** 37 train + 14 test = 51 |
| - **Organs (8 classes in `tissue`):** 0 Unknown, 1 Breast, 2 Kidney, 3 Liver, |
| 4 Prostate, 5 Bladder, 6 Colon, 7 Stomach. Test also includes lung and brain |
| (labelled as tissue=0 Unknown here where not in the 8-class list). |
| - **Ground truth:** single-annotator semantic binary mask (0 = tissue, |
| 1 = nucleus), derived by OR-combining all per-nucleus instance polygons. |
|
|
| ## Columns |
| | Column | Type | Notes | |
| |---|---|---| |
| | `patient` | string | TCGA patient ID (e.g. `TCGA-38-6178-01Z-00-DX1`) | |
| | `tissue` | ClassLabel(8) | Organ label | |
| | `image` | Image (RGB) | 1000x1000 H&E tile | |
| | `mask` | Image (mode `1`) | 1000x1000 binary nuclei mask | |
| | `num_nuclei` | int32 | Instance count used to build the mask | |
|
|
| ## Derivation |
| Source: RationAI/MoNuSeg parquet mirror of the Grand Challenge 2018 data. |
| The `instances` column of the source (a list of per-nucleus binary PIL |
| masks) was merged by logical OR to produce a semantic `mask` column. No |
| other preprocessing. |
|
|
| ## License |
| CC BY-NC-SA 4.0. Underlying WSIs come from TCGA (public NIH data). |
|
|
| ## Citations |
| - Kumar et al., "A Dataset and a Technique for Generalized Nuclear |
| Segmentation for Computational Pathology," IEEE TMI 36(7):1550-1560, 2017. |
| - Kumar et al., "A Multi-organ Nucleus Segmentation Challenge," IEEE TMI, |
| 2019. |
|
|
