3D Fourier Domain Adaptation for Improving CBCT Tooth Segmentation Under Scanner Parameter Shift

Pranjal Sahu, James Fishbaugh, Jared Vicory, Asma Khan, Beatriz Paniagua

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Convolutional neural network based segmentation models have shown success in teeth segmentation from cone beam computed tomography (CBCT) scans. However, trained models often fail to generalize to new acquisitions when scanner protocols shift and upgrade. This problem is well-known by the machine learning community as domain shift. To address this problem in an unsupervised manner, we demonstrate the first time application of 3D Fourier Domain Adaptation of a tooth segmentation model in a source domain for an adapted target domain. Our experiments demonstrate that the proposed domain adaptation method can significantly improve the segmentation performance for the target domain.

Original languageEnglish (US)
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
DOIs
StatePublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: Apr 18 2023Apr 21 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2023-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/TerritoryColombia
CityCartagena
Period4/18/234/21/23

Keywords

  • CBCT
  • CNN
  • Domain Adaption
  • Segmentation
  • Teeth

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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