---
license: cc-by-4.0
task_categories:
  - image-segmentation
task_ids:
  - instance-segmentation
  - semantic-segmentation
language:
  - en
tags:
  - medical-imaging
  - electron-microscopy
  - nuclei-segmentation
  - 3d-segmentation
  - zebrafish
  - neuroscience
pretty_name: NucMM-Z (Neuronal Nuclei from Zebrafish)
size_categories:
  - 1K<n<10K
---

# NucMM-Z Dataset

## Overview

**NucMM-Z** (Neuronal Nuclei from Zebrafish) is a 3D electron microscopy (EM) dataset for nuclei instance segmentation from zebrafish brain tissue.

| Property | Value |
|----------|-------|
| **Modality** | Electron Microscopy (EM) |
| **Task** | Nuclei instance segmentation |
| **Anatomy** | Zebrafish brain |
| **Volume Size** | 64 × 64 × 64 voxels per patch |
| **Train Volumes** | 27 |
| **Val Volumes** | 27 |
| **Total Size** | ~1.09 GB |

## Dataset Structure

```
NucMM-Z/
├── image.tif           # Full raw volume (~1 GB)
├── mask.h5             # Full annotation volume
├── README.txt          # Original readme
├── Image/
│   ├── train/          # 27 training patches (.h5)
│   └── val/            # 27 validation patches (.h5)
└── Label/
    ├── train/          # 27 training labels (.h5)
    └── val/            # 27 validation labels (.h5)
```

## Label Format

- **Instance Segmentation**: Each nucleus has a unique integer ID
- Background: 0
- Typical density: 50-300 nuclei per 64×64×64 volume

## Usage with EasyMedSeg

```python
from dataloader import NucMMZImageDataset, NucMMZVideoDataset

# Image mode (2D slices) - Recommended
dataset = NucMMZImageDataset(split='train')
sample = dataset[0]  # Returns dict with 'image' and 'mask'

# Video mode (3D volumes as frame sequences)
dataset = NucMMZVideoDataset(split='train')
video = dataset[0]  # Returns dict with 'frames' and 'masks'
```

## Benchmark Results (SAM2)

| Mode | Model | Mean Dice | Mean IoU |
|------|-------|-----------|----------|
| **Image** | sam2_hiera_large | **0.3438** | 0.2566 |
| Video | sam2_video_hiera_large | 0.0631 | 0.0425 |

**Recommendation**: Use image mode for this dataset.

## Source

- **Original**: [PyTorch Connectomics NucMM](https://connectomics-bazaar.github.io/proj/NucMM/index.html)
- **Paper**: Wei et al., MICCAI 2020

## License

CC BY 4.0