Combining anatomical constraints and deep learning for 3-D CBCT dental image multi-label segmentation

Jiayu Huang, Hao Yan, Jing Li, H. Milton Stewart, Frank Setzer

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

6 Scopus citations


Machine learning research on medical images is becoming popular as advanced imaging technologies and equipment in medicine become more and more available. Dental Cone-beam Computed Tomography (Dental CBCT), a frequently-used visualization tool for oral diagnosis, provides valuable three-dimensional information, whose development for automation of Dental CBCT analysis, on the other hand, is relatively preliminary. Generally, there are three important characteristics for analyzing Dental CBCT with noisy labels and limited labeled sample size, and availability of oral medicine knowledge. Based on those characteristics, we develop an image segmentation method for Dental CBCT by integrating domain knowledge into deep U-Net for the 3D segmentation. Finally, depending on whether the knowledge can be decomposed into each pixel, the knowledge constraints are classified into two types: separable and non-separable constraints. All knowledge constraints can be represented as a posterior regularization term and solved in different ways in accordance with related types. For separable constraints, the mean-field theory is employed to solve an optimization problem with the independence assumption about the distributions of output variables on each pixel. For non-separable constraints, we propose to combine the importance sampling based approach and the stochastic optimization algorithm. Finally, we propose to formulate the domain knowledge to the learning stage to improve the accuracy and efficiency of automation of Dental CBCT segmentation.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781728191843
StatePublished - Apr 2021
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: Apr 19 2021Apr 22 2021

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference37th IEEE International Conference on Data Engineering, ICDE 2021
CityVirtual, Chania

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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