Piscis

KO | EN

gitlite — search

#python #hacktoberfest #ai #llm #go #machine-learning #macos #windows #awesome #kubernetes #pytorch #mcp #openai #open-source

(★ 29)

An automatic deep learning algorithm for spot detection in fluorescence microscopy images.

Piscis Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Piscis

`piscis` is an automatic deep learning algorithm for spot detection, written in Python using the
[PyTorch](https://pytorch.org/) framework. It is named after the Latin word for fish, as it was designed specifically
for microscopy images generated by RNA fluorescence _in situ_ hybridization (FISH). However, we have found it to be
useful for other imaging methods, such as immunofluorescence (IF) and FISH-based spatial transcriptomics. To learn more
about `piscis`, please read our _Cell Systems_
[paper](https://www.cell.com/cell-systems/abstract/S2405-4712(25)00281-9) or bioRxiv
[preprint](https://doi.org/10.1101/2024.01.31.578123).

This Python package allows users to apply pre-trained models from [Hugging Face](https://huggingface.co/wniu/Piscis) to
both single plane images and z-stacks or to train new models using custom datasets. It provides a simple API for both
training and inference that can be used in traditional Python scripts or [Jupyter](https://jupyter.org/) notebook
environments such as on [Google Colab](https://colab.research.google.com/). It also provides a command line interface
for those who prefer the terminal. For a user-friendly graphical user interface, we have implemented `piscis` as a
[Docker image](https://github.com/arjunrajlaboratory/ImageAnalysisProject/tree/master/workers/annotations/piscis) for
[NimbusImage](https://github.com/arjunrajlaboratory/NimbusImage), a cloud platform for biological image analysis
enabling researchers to interactively visualize their data while leveraging state-of-the-art machine learning
algorithms.

For more information, please refer to the comprehensive documentation available at https://piscis.netlify.app.

## Examples

## Installation

Install `piscis` from [PyPI](https://pypi.org/project/piscis/) with `pip`.
```
pip install piscis
```

## Usage

If you want to use `piscis` with its Python API, check out the [inference example](notebooks/run_piscis.ipynb) and
[training example](notebooks/train_piscis.ipynb) notebooks.

If you want to use `piscis` with its command line interface, run the following commands.
```
# Run Piscis.
piscis predict INPUT_PATH OUTPUT_PATH [OPTIONS]

# Train Piscis.
piscis train MODEL_NAME DATASET_PATH [OPTIONS]
```
To see the full list of options, run `piscis predict --help` or `piscis train --help`.

## Citation

If you use `piscis` in your research, please cite our
[paper](https://www.cell.com/cell-systems/abstract/S2405-4712(25)00281-9).
> Niu, Z., O’Farrell, A., Li, J., Reffsin, S., Jain, N., Dardani, I., Goyal, Y., & Raj, A. (2025). Piscis: A loss
estimator of the F1 score enables accurate spot detection in fluorescence microscopy images via deep learning.
> _Cell Systems_. https://doi.org/10.1016/j.cels.2025.101448
```
@article{Niu2025-Piscis,
    title={Piscis: A loss estimator of the F1 score enables accurate spot detection in fluorescence microscopy images via deep learning},
    author={Niu, Zijian and O’Farrell, Aoife and Li, Jingxin and Reffsin, Sam and Jain, Naveen and Dardani, Ian and Goyal, Yogesh and Raj, Arjun},
    year=2025,
    journal="Cell Systems",
    DOI={10.1016/j.cels.2025.101448}
}
```

## License

`piscis` is licensed under the MIT License. The copyright and permission notices found in the [LICENSE](LICENSE) file
shall be included in all copies or substantial portions of the Software.

Piscis

Piscis Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Piscis

Piscis Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README