Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer.

ABSTRACT: Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for val

SUBMITTER: Li Y 

PROVIDER: S-EPMC9340321 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature