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Clinical Evaluation of a Novel Deep Learning-Based Auto-Segmentation Software: Utility and Potential Pitfalls

2026-01-11

Abstract excerpt

<h4>Background:</h4> Accurate contouring of target volumes and organs at risk is critical for radiotherapy. While deep learning (DL) models offer efficient automation, their generalizability to real-world clinical cases containing anatomical variations and artifacts requires rigorous validation. <h4>Purpose:</h4> To evaluate the clinical accuracy and robustness of RatoGuide, a novel DL-based auto-segmentation so...

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Literature Corpus work
4560f1fe-051f-5100-a046-6b4be04ce5c5
DOI
10.64898/2026.01.08.26343652
Open publication

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Clinical Evaluation of a Novel Deep Learning-Based Auto-Segmentation Software: Utility and Potential PitfallsDOI 10.64898/2026.01.08.26343652
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