RACLAHE_Image_Enhancement_for_CNN_model_segmentation
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
🚀 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/
📋 Requirements
- Docker and Docker Compose
- Medical images in DICOM (
.dcm) or NIfTI (.nii,.nii.gz) format
📁 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
📤 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)
🔧 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
✅ 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
🧬 Algorithm Overview
RACLAHE enhances medical images by:
- Detecting the prostate region using a pre-trained U-Net model
- Applying adaptive histogram equalization to the detected region
- Preserving original characteristics in non-prostatic regions
- 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.
📊 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}
}
🆘 Support
For questions or issues:
- 📖 Read the QUICKSTART.md guide
- 📧 Contact: dimzaridis@gmail.com
- 📄 Paper: https://doi.org/10.1038/s41598-023-27671-8
📜 License
🙏 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.