Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy
Abstract
Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased.Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized...
Paper Details
Title
Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy
Published Date
Jul 1, 2021
Volume
19
Pages
96 - 101
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