Segmentation of Dental Restorations on Panoramic Radiographs Using Deep Learning.

ABSTRACT: Convolutional Neural Networks (CNNs) such as U-Net have been widely used for medical image segmentation. Dental restorations are prominent features of dental radiographs. Applying U-Net on the panoramic image is challenging, as the shape, size and frequency of different restoration types vary. We hypothesized that models trained on smaller, equally spaced rectangular image crops (tiles) of the panoramic would outperform models trained on the full image. A total of 1781 panoramic radiographs were annotated pixelwise for fillings, crowns, and root canal fillings by dental experts. We used different numbers of tiles for our experiments. Five-times-repeated three-fold cross-validation was used for model evaluation. Training with more tiles improved model performance and accelerated convergence

SUBMITTER: Rohrer C 

PROVIDER: S-EPMC9221749 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature