Retrospective Comparison of Geometrical Accuracy among Atlas-based Auto-segmentation, Deep Learning Auto-segmentation, and Deformable Image Registration in the Treatment Replanning for Adaptive Radiotherapy of Head-and-Neck Cancer

Aims: This study aimed to evaluate the geometrical accuracy of atlas-based auto-segmentation (ABAS), deformable image registration (DIR), and deep learning auto-segmentation (DLAS) in adaptive radiotherapy (ART) for head-and-neck cancer (HNC). Subjects and Methods: Seventeen patients who underwent r...

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Ngā kaituhi matua:	Yukari Nagayasu, Shoki Inui, Yoshihiro Ueda, Akira Masaoka, Masahide Tominaga, Masayoshi Miyazaki, Koji Konishi
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Wolters Kluwer Medknow Publications 2024-09-01
Rangatū:	Journal of Medical Physics
Ngā marau:	adaptive radiotherapy atlas-based auto-segmentation automatic segmentation deep learning auto-segmentation deformable image registration head and neck
Urunga tuihono:	https://journals.lww.com/10.4103/jmp.jmp_39_24
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Retrospective Comparison of Geometrical Accuracy among Atlas-based Auto-segmentation, Deep Learning Auto-segmentation, and Deformable Image Registration in the Treatment Replanning for Adaptive Radiotherapy of Head-and-Neck Cancer

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