Fully automated quantitative cephalometry using convolutional neural networks
Author:
Affiliation:
1. Baidu USA, 1195 Bordeaux Drive, Sunnyvale, California 94089, United States
2. Stanford University, Department of Radiation Oncology, School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305, United States
Publisher
SPIE-Intl Soc Optical Eng
Subject
Radiology Nuclear Medicine and imaging
Reference37 articles.
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4. Discrimination of bony structures in cephalograms for automatic landmark detection;Kafieh,2008
5. Automatic cephalometric landmark detection using Zernike moments and template matching
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