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

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

Producción científica: Conference contribution

1 Cita (Scopus)

Resumen

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.

Idioma originalEnglish (US)
Título de la publicación alojada2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
EditorialIEEE Computer Society
ISBN (versión digital)9781665473583
DOI
EstadoPublished - 2023
Evento20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duración: abr 18 2023abr 21 2023

Serie de la publicación

NombreProceedings - International Symposium on Biomedical Imaging
Volumen2023-April
ISSN (versión impresa)1945-7928
ISSN (versión digital)1945-8452

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
País/TerritorioColombia
CiudadCartagena
Período4/18/234/21/23

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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