Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

Mattea L. Welch*, Chris McIntosh, Tom G. Purdie, Leonard Wee, Alberto Traverso, Andre Dekker, Benjamin Haibe-Kains, David A. Jaffray

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Web of Science)
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Enabling automated pipelines, image analysis and big data methodology in cancer clinics requires thorough understanding of the data. Automated quality assurance steps could improve the efficiency and robustness of these methods by verifying possible data biases. In particular, in head and neck (H&N) computed-tomography (CT) images, dental artifacts (DA) obscure visualization of structures and the accuracy of Hounsfield units; a challenge for image analysis tasks, including radiomics, where poor image quality can lead to systemic biases. In this work we analyze the performance of three-dimensional convolutional neural networks (CNN) trained to classify DA statuses. 1538 patient images were scored by a single observer as DA positive or negative. Stratified five-fold cross validation was performed to train and test CNNs using various isotropic resampling grids (64(3), 128(3) and 256(3)), with CNN depths designed to produce 32(3), 16(3), and 8(3) machine generated features. These parameters were selected to determine if more computationally efficient CNNs could be utilized to achieve the same performance. The area under the precision recall curve (PR-AUC) was used to assess CNN performance. The highest PR-AUC (0.92 +/- 0.03) was achieved with a CNN depth = 5, resampling grid = 256. The CNN performance with 256(3) resampling grid size is not significantly better than 64(3) and 128(3) after 20 epochs, which had PR-AUC = 0.89 +/- 0.03 (p -value = 0.28) and 0.91 +/- 0.02 (p -value = 0.93) at depths of 3 and 4, respectively. Our experiments demonstrate the potential to automate specific quality assurance tasks required for unbiased and robust automated pipeline and image analysis research. Additionally, we determined that there is an opportunity to simplify CNNs with smaller resampling grids to make the process more amenable to very large datasets that will be available in the future.

Original languageEnglish
Article number015005
Number of pages9
JournalPhysics in Medicine and Biology
Issue number1
Publication statusPublished - Jan 2020


  • CT imaging
  • HEAD
  • automation
  • deep learning
  • dental artifacts
  • quality classification

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