Automated delineation of head and neck organs at risk using synthetic MRI‐aided mask scoring regional convolutional neural network

Purpose Auto‐segmentation algorithms offer a potential solution to eliminate the labor‐intensive, time‐consuming, and observer‐dependent manual delineation of organs‐at‐risk (OARs) in radiotherapy treatment planning. This study aimed to develop a deep learning‐based automated OAR delineation method...

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Veröffentlicht in:	Medical physics (Lancaster) 2021-10, Vol.48 (10), p.5862-5873
Hauptverfasser:	Dai, Xianjin, Lei, Yang, Wang, Tonghe, Zhou, Jun, Roper, Justin, McDonald, Mark, Beitler, Jonathan J., Curran, Walter J., Liu, Tian, Yang, Xiaofeng
Format:	Artikel
Sprache:	eng
Schlagworte:	deep learning Head - diagnostic imaging Head and Neck Neoplasms - diagnostic imaging Head and Neck Neoplasms - radiotherapy Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging multi‐organ segmentation Neural Networks, Computer Organs at Risk Radiotherapy Planning, Computer-Assisted synthetic MRI
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