RACLAHE: Region Adaptive MR Image Enhancement for CNN-based Segmentation

RACLAHE (Region-Adaptive Contrast Limited Adaptive Histogram Equalization) is an image enhancement method specifically designed for improving CNN-based segmentation of the prostate and prostatic zones in T2-Weighted MR images.

This repository supports the original publication in Nature's Scientific Reports: "Region-Adaptive Magnetic Resonance Image Enhancement for improving CNN-based segmentation of the prostate and prostatic zones" doi:10.1038/s41598-023-27671-8

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🚀 Quick Start

📖 See QUICKSTART.md for detailed step-by-step instructions

# 1. Prepare your data
mkdir -p input output shared
cp -r /path/to/your/patients/* input/

# 2. Run processing
docker-compose up --build

# 3. Get results from output/RACLAHE OUTPUT/

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📋 Requirements

• Docker and Docker Compose
• Medical images in DICOM (.dcm) or NIfTI (.nii, .nii.gz) format

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📁 Input Data Format

Organize your data with one folder per patient:

DICOM Series

input/
├── patient001/
│   ├── slice001.dcm
│   ├── slice002.dcm
│   └── ...
└── patient002/
    ├── slice001.dcm
    └── ...

NIfTI Files

input/
├── patient001/
│   └── scan.nii.gz
└── patient002/
    └── scan.nii.gz

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📤 Output Format

Enhanced images are saved in the same format as input:

output/RACLAHE OUTPUT/
├── patient001/
│   ├── image_1.dcm      (if DICOM input)
│   ├── image_2.dcm
│   └── ...
└── patient002/
    └── patient002.nii.gz (if NIfTI input)

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🔧 Usage

With Docker Compose (Recommended)

docker-compose up --build

With Docker

# Build
docker build -t raclage:3.0 .

# Run
docker run \
  -v $(pwd)/input:/home/ds/datasets:ro \
  -v $(pwd)/output:/home/ds/persistent-home \
  raclage:3.0

Custom Paths

docker run \
  -e INPUT_DIR=/custom/input \
  -e OUTPUT_DIR=/custom/output \
  -v /your/input:/custom/input:ro \
  -v /your/output:/custom/output \
  raclage:3.0

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✅ EUCAIM Platform Compliance

This image is fully EUCAIM-compliant:

• ✅ Runs as non-root user ds (uid 1000, gid 1000)
• ✅ Uses standard EUCAIM volume mount points:
  • /home/ds/datasets - Input datasets (read-only)
  • /home/ds/persistent-home - Output storage
  • /home/ds/persistent-shared-folder - Shared storage
• ✅ No internet access required at runtime
• ✅ Batch processing optimized for medical imaging workflows

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🧬 Algorithm Overview

RACLAHE enhances medical images by:

1. Detecting the prostate region using a pre-trained U-Net model
2. Applying adaptive histogram equalization to the detected region
3. Preserving original characteristics in non-prostatic regions
4. Combining enhanced and unenhanced regions for the final output

This targeted approach improves CNN segmentation performance by 3-9% (Dice score) across different prostatic regions.

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📊 Citation

If you use RACLAHE in your research, please cite:

@article{zaridis2023region,
  title={Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones},
  author={Zaridis, Dimitrios I and Mylona, Eugenia and Tachos, Nikolaos and Pezoulas, Vasileios C and Grigoriadis, Grigorios and Tsiknakis, Nikos and Marias, Kostas and Tsiknakis, Manolis and Fotiadis, Dimitrios I},
  journal={Scientific Reports},
  volume={13},
  number={1},
  pages={714},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

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🆘 Support

For questions or issues:

• 📖 Read the QUICKSTART.md guide
• 📧 Contact: dimzaridis@gmail.com
• 📄 Paper: https://doi.org/10.1038/s41598-023-27671-8

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📜 License

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🙏 Acknowledgements

This work is supported by the ProCancer-I project, funded by the European Union's Horizon 2020 research and innovation program under grant agreement No 952159.