Datasets:
Tasks:
Image Segmentation
Languages:
English
Size:
1K - 10K
Tags:
medical-imaging
electron-microscopy
nuclei-segmentation
3d-segmentation
zebrafish
neuroscience
License:
File size: 2,311 Bytes
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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
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