---
license: cc-by-4.0
task_categories:
- image-segmentation
modality:
- microscopy
language: []
tags:
- microscopy
- fluorescence
- 3D
- nuclei-segmentation
- instance-segmentation
- embryonic-imaging
- NeurIPS-2023
pretty_name: NIS3D
size_categories:
- n<1K
dataset_info:
  features:
  - name: sample_id
    dtype: string
  - name: subset
    dtype: string
  - name: num_slices
    dtype: int32
  - name: height
    dtype: int32
  - name: width
    dtype: int32
  - name: cells_in_middle_slice
    dtype: int32
  - name: data_middle_slice
    dtype: image
  - name: gt_middle_slice
    dtype: image
  - name: confidence_middle_slice
    dtype: image
  - name: overlay_middle_slice
    dtype: image
  splits:
  - name: train
    num_bytes: 41481448
    num_examples: 24
  download_size: 41487097
  dataset_size: 41481448
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# NIS3D — A Completely Annotated Benchmark for Dense 3D Nuclei Image Segmentation

NIS3D is a dense 3D nuclei segmentation benchmark for fluorescence microscopy of embryonic tissue. It provides 6 large-volume 3D images (Zebrafish, Drosophila, Mus Musculus) with **22,000+ manually annotated cells** vetted by three independent annotators, accompanied by per-cell confidence scores derived from inter-annotator agreement.

Published at **NeurIPS 2023 (Datasets and Benchmarks Track)**.

## Dataset Summary

| Field | Details |
|---|---|
| Modality | 3D fluorescence microscopy (volumetric TIFF stacks) |
| Target | Cell nuclei (instance segmentation) |
| Species | Zebrafish, Drosophila, Mus Musculus (mouse) |
| Samples | 6 (Zebrafish_1, Zebrafish_2, Drosophila_1, Drosophila_2, MusMusculus_1, MusMusculus_2) |
| Annotated cells | 22,000+ |
| File format | Multi-page TIFF (`.tif`), opens with Fiji/ImageJ or `tifffile` |
| License | CC-BY-4.0 |
| Source | Zenodo: https://zenodo.org/records/11456029 (DOI 10.5281/zenodo.11456029) |
| GitHub | https://github.com/yu-lab-vt/NIS3D |

## Per-Sample Files

Each sample folder under `NIS3D/` contains:

| File | Role |
|---|---|
| `data.tif` | Raw 3D fluorescence volume |
| `GroundTruth.tif` (or `gt.tif` for MusMusculus_2) | **Recommended ground-truth instance label map** (different integers = different cells, 0 = background) |
| `ConfidenceScore.tif` (or `scoreOfConfidence.tif` for MusMusculus_2) | Per-cell reliability map: 0 = background, 1 = "undefined mask" (boundaries indeterminate, ignored at evaluation), 2 = 1/3 annotator agreement, 3 = 2/3, 4 = 3/3 (full consensus) |
| `visulize.tif` | Composite visualization (raw + GT + confidence) — display only |
| `Info.txt` | Acquisition details (microscope, resolution, pre-processing, etc.) |

The single `GroundTruth.tif` is the consensus across three annotators; per-annotator disagreement is encoded into `ConfidenceScore.tif` rather than rival GT files. **At evaluation, predictions falling on confidence-1 voxels should be ignored** (neither TP nor FP) because boundaries there are too weak for any annotator to delineate.

## Splits

The Zenodo archive ships **two suggestive split schemes** under `suggestive splitting/`, in addition to the canonical full-size 6 samples in `NIS3D/`:

| Split scheme | Description |
|---|---|
| `cross-image/` | Leave-one-per-species: train on *_2, test on *_1 (full-size volumes) |
| `in-image/` | Each of the 6 samples is cropped into disjoint train/test halves |

Both schemes are advisory — researchers may use the canonical `NIS3D/` folder and define their own splits.

## Per-Sample Stats

| Sample | data.tif size | GroundTruth.tif size | Cells (approx.) |
|---|---|---|---|
| Zebrafish_1 | 1.25 GB | 68 MB | (high density) |
| Zebrafish_2 | 45 MB | 6.5 MB | |
| Drosophila_1 | 382 MB | 28 MB | |
| Drosophila_2 | 386 MB | 17 MB | |
| MusMusculus_1 | 554 MB | 45 MB | |
| MusMusculus_2 | 273 MB | 47 MB | |

Per-sample acquisition details (microscope, resolution, pre-processing) are in each `Info.txt`.

## Citation

```bibtex
@inproceedings{zheng2023nis3d,
  title     = {NIS3D: A Completely Annotated Benchmark for Dense 3D Nuclei Image Segmentation},
  author    = {Zheng, Wei and Peng, James Cheng and Hou, Zeyuan and Lyu, Boyu and Wang, Mengfan and Mi, Xuelong and Qiao, Shuoxuan and Wan, Yinan and Yu, Guoqiang},
  booktitle = {Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year      = {2023},
  url       = {https://openreview.net/forum?id=Icxwnu9hcO}
}

@dataset{zheng2024nis3dzenodo,
  title     = {NIS3D},
  author    = {Zheng, Wei and Peng, James Cheng and Hou, Zeyuan and Lyu, Boyu and Wang, Mengfan and Mi, Xuelong and Qiao, Shuoxuan and Wan, Yinan and Yu, Guoqiang},
  year      = {2024},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.11456029},
  url       = {https://doi.org/10.5281/zenodo.11456029}
}
```