Datasets:

MedOtter
/

2018-Data-Science-Bowl

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 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