Nuclear Segmentation Pipelines

The GoNuclear repository hosts the code and guides for the pipelines used in the paper A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context. It is structured in to four folders:

• stardist/ contains a 3D StarDist training and inference pipeline, run-stardist.
• plantseg/ contains configuration files for training and inference with PlantSeg.
• cellpose/ contains scripts for training and inference with Cellpose.
• evaluation/ contains modules for evaluating the segmentation results.

and are described in GoNuclear documentation 📖.

Data and Models

Please go to BioImage Archive S-BIAD1026 for the training data and models. I organised them in the following structure:

Training data
├── 2d/
│   ├── isotropic/
│   │   ├── gold/
│   │   └── initial/
│   └── original/
│       ├── gold/
│       └── README.txt
└── 3d_all_in_one/
    ├── 1135.h5
    ├── 1136.h5
    ├── 1137.h5
    ├── 1139.h5
    └── 1170.h5

Models
├── cellpose/
│   ├── cyto2_finetune/
│   │   └── gold/
│   ├── nuclei_finetune/
│   │   ├── gold/
│   │   └── initial/
│   └── scratch_trained/
│       └── gold/
├── plantseg/
│   └── 3dunet/
│       ├── gold/
│       ├── initial/
│       ├── platinum/
│       └── train_example.yml
└── stardist/
    ├── resnet/
    │   ├── gold/
    │   ├── initial/
    │   └── platinum/
    ├── train_example.yml
    └── unet/
        └── gold/

Citation

If you find this work useful, please cite our paper and the respective tools' papers:

@article{vijayan2024deep,
  title={A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context},
  author={Vijayan, Athul and Mody, Tejasvinee Atul and Yu, Qin and Wolny, Adrian and Cerrone, Lorenzo and Strauss, Soeren and Tsiantis, Miltos and Smith, Richard S and Hamprecht, Fred A and Kreshuk, Anna and others},
  journal={Development},
  volume={151},
  number={14},
  year={2024},
  publisher={The Company of Biologists}
}