DeepDoseSens: Evaluating Sensitivity of Deep Learning-Based Radiotherapy Dose Prediction to Organs-at-Risk Segmentation Variability
This repository accompanies our paper:
"How Sensitive Are Deep Learning Based Radiotherapy Dose Prediction Models To Variability In Organs At Risk Segmentation?"
Accepted at the 20th International Symposium on Biomedical Imaging (ISBI), 2023.
Authors: Amith Kamath, Robert Poel, Jonas Willmann, Nicolaus Andratschke, Mauricio Reyes
See a short video description of this work here:
This project investigates the robustness of deep learning models for radiotherapy dose prediction in glioblastoma patients, focusing on how variability in organs-at-risk (OAR) segmentation affects model performance. We introduce a controlled perturbation framework to simulate realistic segmentation variations and assess their impact on dose prediction accuracy.
- Controlled Perturbation Framework: Developed a method to simulate realistic variations in OAR segmentations.
- Robustness Assessment: Analyzed how segmentation variability influences dose prediction.
- Model Comparison: Evaluated multiple deep learning models for robustness against OAR perturbations.
- Data: Glioblastoma patient CT scans, OAR segmentations, and corresponding dose distributions.
- Perturbation Techniques: Applied geometric deformations and noise to mimic segmentation variability.
- Model Training: Used CNN-based architectures trained on original segmentations and tested on perturbed data.
- Metrics: Evaluated using Mean Absolute Error (MAE), Dose-Volume Histogram (DVH) differences, and other relevant metrics.
- Python 3.8+
- PyTorch
- MONAI
- NumPy
- SciPy
- Matplotlib
git clone https://github.com/amithjkamath/deepdosesens.git
cd deepdosesens
pip install -r requirements.txtIf this is useful in your research, please consider citing:
@inproceedings{kamath2023doseprediction,
title={How sensitive are deep learning based radiotherapy dose prediction models to variability in Organs At Risk segmentation?},
author={Kamath, Amith and Poel, Robert and Willmann, Jonas and Andratschke, Nicolaus and Reyes, Mauricio},
booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)},
pages={1--4},
year={2023},
organization={IEEE}
}
Major props to the code and organization in https://github.com/LSL000UD/RTDosePrediction, which is what this model is based on (looks like this repo is not maintained/available anymore!)