Semantic segmentation of mouse jaws using convolutional neural networks

Victoria Cooley, Stuart R. Stock, William Guise, Adya Verma, Tomas Wald, Ophir Klein, Derk Joester

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

1 Scopus citations


Genetic mouse models are a powerful tool to study congenital defects in tooth enamel. Comparison of micro-computed tomography (micro-CT) reconstructions between mutant mice and wild-type (WT) controls requires an automated method of segmenting the mineralized tissues of the jaw. However, traditional segmentation methods are easily frustrated by mineral gradients, especially those in forming enamel in the continuously growing incisor. Herein, we demonstrate that convolutional neural networks (CNNs) with UNET architecture can be trained to accurately segment WT jaws into seven classes (molar enamel, incisor dentin, molar dentin, bone, background, Al wire, and either incisor enamel or ectopic mineral). We then evaluate the performance of CNNs trained on WT jaws on two mutant phenotypes, with and without additional cross-training, using CNNs trained only on mutant data for comparison, and find that cross-training provides significant improvement. Finally, we demonstrate that segmentation using a cross-trained network enables extracting metrics for quantitative comparison of mineral gradients in the incisor. Our results show that the CNN-based segmentation and quantification pipeline is a versatile tool that will empower enamel researchers, help delineate mechanisms of disease, and enable the development of new approaches of intervention.

Original languageEnglish (US)
Title of host publicationDevelopments in X-Ray Tomography XIII
EditorsBert Muller, Ge Wang
ISBN (Electronic)9781510645189
StatePublished - 2021
EventDevelopments in X-Ray Tomography XIII 2021 - San Diego, United States
Duration: Aug 1 2021Aug 5 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceDevelopments in X-Ray Tomography XIII 2021
Country/TerritoryUnited States
CitySan Diego


  • Dental enamel
  • Machine learning
  • Neural network
  • Semantic segmentation
  • X-ray computed tomography

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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