README.md

metadata

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

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
• Paper: Wei et al., MICCAI 2020

License

CC BY 4.0