dpat 2.0.0

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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 and unzip somewhere. On Linux/macOS, vips is included with the installation steps below.

OpenSlide

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

CUDA

To do deep learning on CUDA enabled accelerators, follow installation instructions on pytorch.org. Run nvidia-smi to see if CUDA is available.

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Installation

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

1. Clone this repository and change directories

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

2. Create a new conda environment and activate it.

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

3. Install dependencies from environment.yml.

   conda env update -f environment.yml
   

4. Windows only: if you use this library for use in scripts, make sure libvips is available, see Prerequisites#libvips. If using this library in a script, make sure to properly install the package with

   import dpat
   dpat.install_windows("path/to/vips/bin")
   

5. If you use this library for deep learning and want to use CUDA-enabled Pytorch, follow instructions on pytorch.org. Make sure CUDA is available, see Prerequisites#CUDA.
6. Install dpat in editable mode with

   pip install -e .
   

7. 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 batch -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 batch [OPTIONS]

Options:
  -i, --input-dir TEXT         Input directory where to find the images to be
                               converted.  [default: .]
  -o, --output-dir TEXT        Output directory where place converted files.
                               [default: ./converted]
  -e, --output-ext [tiff|tif]  Extension to convert to.  [required]
  -w, --num-workers INTEGER    Number of workers that convert the images in
                               parallel.  [default: 4]
  -c, --chunks INTEGER         Number of chunks distributed to every worker.
                               [default: 30]
  --trust                      Trust the source of the images.
  --skip-existing              Skip existing output files.
  --help                       Show this message and exit.

Creating splits

To create train-val-test splits linking paths of images to splits with IMAGE_DIR, output the splits to OUTPUT_DIR, with labels PATH_TO_LABELS_FILE, and dataset name NAME run

dpat splits create -i IMAGE_DIR -o OUTPUT_DIR -l PATH_TO_LABELS_FILE -n NAME

To filter diagnoses that exactly match diseases, use e.g. -f medulloblastoma -f "pilocytic astrocytoma". To filter filenames that match certain values, use a glob pattern. E.g. -y *slow.tiff to only include images ending with slow.tiff. To exclude filenames that match certaine values, use a glob pattern with -x. Exclusion is performed on the set specified by inclusion.

Usage: dpat splits create [OPTIONS]

Options:
  -i, --input-dir TEXT   Input directory where to find the images.  [required]
  -l, --labels TEXT      Path to labels file.  [required]
  -n, --name TEXT        Name of dataset.  [required]
  -o, --output-dir TEXT  Directory where to put the splits.  [default: splits]
  --overwrite            Overwrite folds in output dir, if available.
  -y, --include TEXT     Glob pattern to include files from `input-dir`
                         [default: *.*]
  -x, --exclude TEXT     Glob pattern to exclue files from `input-dir`,
                         included with `--include`
  -f, --filter TEXT      Filter a diagnosis. For multiple diagnoses, use `-f 1
                         -f 2`.
  --help                 Show this message and exit.

Logging

When using the package as a library, if needed, logging can be turned off with

logging.getLogger('dpat').propagate = False

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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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