---
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.