---
license: cc0-1.0
pretty_name: 2018 Data Science Bowl (BBBC038) - Nuclei Segmentation
task_categories:
- image-segmentation
tags:
- medical
- biomedical
- microscopy
- histopathology
- fluorescence
- nuclei
- cell-segmentation
- bbbc038
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: stage1_test
    path: data/stage1_test-*
  - split: stage2_test
    path: data/stage2_test-*
---

# 2018 Data Science Bowl (BBBC038) - Nuclei Segmentation

2D light-microscopy **cell-nucleus segmentation** assembled across many imaging
experiments (humans, mice, flies; 22 cell types, 15 resolutions, 30+ experiments).
The collection deliberately spans **multiple modalities**: fluorescence
(DAPI / Hoechst), brightfield **H&E histopathology**, and other brightfield -
making it a standard cross-modality nuclei-segmentation benchmark.

This is the official **BBBC038v1** release (Broad Bioimage Benchmark Collection),
the same data used in the Kaggle 2018 Data Science Bowl. **License: CC0 / public
domain.**

## Contents & splits

| Split | Images | Nuclei | Ground-truth source |
|-------|-------:|-------:|---------------------|
| `train`        (stage1_train) | 670 | 29,461 | native per-nucleus PNG instance masks |
| `stage1_test`  (stage1_test)  |  65 |  4,152 | RLE in `stage1_solution.csv` (post-competition) |
| `stage2_test`  (stage2_test)  | 106 |  3,716 | RLE in `stage2_solution_final.csv` (post-competition) |
| **Total** | **841** | **37,329** | |

**Faithful-naming notes**
- Most papers cite "DSB2018" = `stage1_train` (670) only, since that is the only
  split distributing *native* instance masks. This repo ships the **full 3-stage**
  set; the test-stage GT was decoded from the official solution-CSV RLE.
- The raw `stage2_test_final` archive contains ~3,019 images, but only **106 are
  scored** - the rest are intentional **decoys** flagged `Usage=Ignored`. **Only
  the 106 scored images are included here.**

## Ground truth

`mask` is a **binary semantic** nucleus mask (mode `L`, values `{0, 255}`): the
**union of all per-nucleus instances**. For `train` it is the union of the native
per-nucleus PNG masks; for the test splits it is the union of the RLE-decoded
nuclei. The RLE decoder was validated against the native train masks
(pixel agreement = 1.000000). The original per-nucleus **instance** masks remain
available at [BBBC038](https://bbbc.broadinstitute.org/BBBC038) for
instance-segmentation use.

## Columns

| Column | Type | Notes |
|--------|------|-------|
| `image_id`     | string | source hash id |
| `image`        | Image  | RGB (RGBA fluorescence normalized to RGB) |
| `mask`         | Image  | binary semantic, `{0,255}` |
| `split`        | string | `stage1_train` / `stage1_test` / `stage2_test` |
| `num_nuclei`   | int32  | nuclei in this image |
| `height`,`width` | int32 | image dimensions |
| `usage`        | string | `null` (train) / `Public` (s1) / `Private` (s2) |
| `is_grayscale` | bool   | derived (R==G==B): fluorescence/brightfield vs H&E color |

`metadata.xlsx` (repo root) is the official 43-row per-experiment provenance table
(cell type, stain, SNR, resolution).

## Provenance, overlap & integrity

- **Provenance:** official BBBC038v1 (Broad Institute), CC0. Counts reconcile with
  the paper (670 / 65 / 106).
- **Overlap (leakage hazards):** a small fraction of images overlap **BBBC039**.
  The H&E subset shares source-level (TCGA-derived) lineage with H&E nuclei sets
  such as MoNuSeg / PanNuke, though no individually-confirmed shared images.
- **Curated collection:** assembled from 30+ independent experiments / donor labs.

## Citation

Caicedo, J.C., Goodman, A., Karhohs, K.W., et al. *Nucleus segmentation across
imaging experiments: the 2018 Data Science Bowl.* **Nature Methods** 16(12),
1247-1253 (2019). doi:10.1038/s41592-019-0612-7