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Realistic high-resolution lateral cephalometric radiography generated by progressive growing generative adversarial network and quality evaluations

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Abstract
Realistic image generation is valuable in dental medicine, but still challenging for generative adversarial networks (GANs), which require large amounts of data to overcome the training instability. Thus, we generated lateral cephalogram X-ray images using a deep-learning-based progressive growing GAN (PGGAN). The quality of generated images was evaluated by three methods. First, signal-to-noise ratios of real/synthesized images, evaluated at the posterior arch region of the first cervical vertebra, showed no statistically significant difference (t-test, p=0.211). Second, the results of an image Turing test, conducted by non-orthodontists and orthodontists for 100 randomly chosen images, indicated that they had difficulty in distinguishing whether the image was real or synthesized. Third, cephalometric tracing with 42 landmark points detection, performed on real and synthesized images by two expert orthodontists, showed consistency with mean difference of 2.08 +/- 1.02 mm. Furthermore, convolutional neural network-based classification tasks were used to classify skeletal patterns using a real dataset with class imbalance and a dataset balanced with synthesized images. The classification accuracy for the latter case was increased by 1.5%/3.3% at internal/external test sets, respectively. Thus, the cephalometric images generated by PGGAN are sufficiently realistic and have potential to application in various fields of dental medicine.
Author(s)
김민규김성철김민지배현진박재우김남국
Issued Date
2021
Type
Article
Keyword
ClassificationComputer scienceDeep learningDentistryExperimental models of diseaseInformation technologyNeural networksOrthodonticsRadiographyStatistical analysisVertebrae
DOI
10.1038/s41598-021-91965-y
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7391
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_6c379c130b4846fba9ba730e1fc708a4&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Realistic%20high-resolution%20lateral%20cephalometric%20radiography%20generated%20by%20progressive%20growing%20generative%20adversarial%20network%20and%20quality%20evaluations&offset=0&pcAvailability=true
Publisher
SCIENTIFIC REPORTS
Location
영국
Language
영어
ISSN
2045-2322
Citation Volume
11
Citation Number
1
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
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