dpat 0.4.1

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Deep Learning for Higher Harmonic Generation Microscopy

Deep learning utilities for higher harmonic generation microscopy images.

This project is a deep learning application to classify various pediatric brain tumours from higher harmonic generation microscopy images.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Contributing
5. License
6. Contact
7. Acknowledgments

About The Project

The project aims to do deep learning classification on higher harmonic generation (HHG) microscopy images of pediatric brain tumours.

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Built With

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Getting Started

This section includes instructions on setting up the project locally.

Prerequisites

Conda

For package management, it is advised to use a conda package manager. The author recommends Miniforge or Mambaforge.

vips

This project depends on dlup (automatically installed), which depends on vips. On Windows, vips needs to be installed locally. Download the latest libvips Windows binary, unzip, and add the path to the vips\bin to project.ini.

OpenSlide

Vips comes with OpenSlide. It is not needed to install OpenSlide separately.

Installation

Run the following commands from a conda enabled shell (such as Miniforge Prompt, if Miniforge/Mambaforge is installed).

1. Make sure libvips is available, see Prerequisites.
2. Clone this repository and change directories

   git clone https://github.com/siemdejong/dpat.git dpat && cd dpat
   

3. Create a new conda environment and activate it.

   conda create -n <env_name>
   conda activate <env_name>
   

4. Install dependencies from environment.yml.

   conda env update -f environment.yml
   

5. Install dpat in editable mode with

   pip install -e .
   

6. Verify installation

   python -c "import dpat"
   

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Usage

Converting images

To convert all images from directory INPUT_DIR, and output the images as TIFF in OUTPUT_DIR, run

dpat convert bulk -i INPUT_DIR -o OUTPUT_DIR -e tiff

Large images need to be trusted against decompression bomb DOS attack. Use the --trust flag. To skip images that were already converted to the target extension, use --skip-existing.

NOTE: If converting to tiff, the input images are assumed to contain the reference to the scanning program, which must be in {200slow, 300slow, 300fast}.

usage: dpat convert bulk [-h] --input-dir INPUT_DIR [--output-dir OUTPUT_DIR] --output-ext {tiff,tif} [--num-workers NUM_WORKERS] [--chunks CHUNKS] [--trust | --no-trust] [--skip-existing | --no-skip-existing]

optional arguments:
  -h, --help            show this help message and exit
  --input-dir INPUT_DIR, -i INPUT_DIR
                        Input directory where to find the images to be converted.
  --output-dir OUTPUT_DIR, -o OUTPUT_DIR
                        Output directory where place converted files.
  --output-ext {tiff,tif}, -e {tiff,tif}
                        Extension to convert to.
  --num-workers NUM_WORKERS
                        Number of workers that convert the images in parallel.
  --chunks CHUNKS       Number of chunks distributed to every worker.
  --trust, --no-trust   Trust the source of the images.
  --skip-existing, --no-skip-existing
                        Skip existing output files.

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Contributing

Contribute using the following steps.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the GNU General Public License v3.0. See LICENSE for more information.

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Contact

Siem de Jong - linkedin.com/in/siemdejong - [e-mail]

Project Link: https://github.com/siemdejong/dpat

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