| --- |
| 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 |
|
|
