General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis

Purpose To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well‐established deep convolutional neural network (DCNN). Meth...

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Veröffentlicht in:	Medical physics (Lancaster) 2022-03, Vol.49 (3), p.1686-1700
Hauptverfasser:	Amjad, Asma, Xu, Jiaofeng, Thill, Dan, Lawton, Colleen, Hall, William, Awan, Musaddiq J., Shukla, Monica, Erickson, Beth A., Li, X. Allen
Format:	Artikel
Sprache:	eng
Schlagworte:	Abdomen - diagnostic imaging adaptive radiation therapy CT‐based autosegmentation Deep Learning Head and Neck Neoplasms - diagnostic imaging Head and Neck Neoplasms - radiotherapy Humans Image Processing, Computer-Assisted - methods Male Organs at Risk Pelvis - diagnostic imaging radiation therapy planning Radiotherapy Planning, Computer-Assisted - methods
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