Volumetric Modulated Arc Therapy Dose Distribution Prediction for Breast Cancer Patients: CNN Approach

This paper aims to explore popular CNN architectures for the generation of patient-specific dose distributions for Volumetric Modulated Arc Therapy (VMAT) radiotherapy satisfying clinical dose-volume constraints. Only organs at risk (OARs) and planning target volume (PTV) segmentations are required...

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Bibliographic Details
Main Authors: Krawczyk, Zuzanna, Szmurlo, Robert, Zawadzki, Pawel, Kot, Estera, Starzynski, Jacek, Zawadzka, Anna
Format: Conference Proceeding
Language:English
Subjects:
Breast cancer
CNN
Convolutional neural networks
deep learning
dose distribution
Medical treatment
Network architecture
Neural networks
Planning
radiotherapy
Training
VMAT
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Summary:This paper aims to explore popular CNN architectures for the generation of patient-specific dose distributions for Volumetric Modulated Arc Therapy (VMAT) radiotherapy satisfying clinical dose-volume constraints. Only organs at risk (OARs) and planning target volume (PTV) segmentations are required as input. The outcome of the research is selecting the optimal network architecture and assessing training parameters. The U-net, \mathbf{ResNet}+\mathbf{U-net},\mathbf{ResNet}+\mathbf{PSPNet} networks are analyzed. The \mathbf{Resnet}+\mathbf{U-net} model was chosen as the most suited to the task. The similarity measures such as mIoU and mDice coefficient as well as the dose-volume constraints in PTV and selected OARs are evaluated. The results confirm that the \mathbf{Reset}+\boldsymbol{U} -net network is capable of generating patient-specific dose distribution and can be used as a valuable assistance during clinical radiotherapy planning procedures.
ISSN:2161-4407
DOI:10.1109/IJCNN52387.2021.9533826