2022
DOI: 10.48550/arxiv.2202.01866
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Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks

Abstract: Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients. Several deep learning based segmentation algorithms have been developed in recent years, however, U-Net remains the de facto algorithm designed specifically for biomedical image segmentation and has spawned many variants with known weaknesses. In this study, our goal is to present simple architectural changes in U-Net to improve its accuracy and generalization prope… Show more

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