Datasets:
metadata
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,