Marketing

Benchmarking

Set (abstract data type)

Data mining

Computer science

Business

Image segmentation

Segmentation

Pattern recognition (psychology)

Data set

Artificial intelligence

Programming language

Deep learning

Market segmentation

Convolutional neural network

Computer vision

Expert Systems with Applications

This review presents an in-depth study of the literature on segmentation methods applied in dental imaging. Several works on dental image segmentation were studied and categorized according to the type of method (region-based, threshold-based, cluster-based, boundary-based or watershed-based), type of X-ray images analyzed (intra-oral or extra-oral), and characteristics of the data set used to evaluate the methods in each state-of-the-art work. We found that the literature has primarily focused on threshold-based segmentation methods (54%). 80% of the reviewed articles have used intra-oral X-ray images in their experiments, demonstrating preference to perform segmentation on images of already isolated parts of the teeth, rather than using extra-oral X-rays, which also show tooth structure of the mouth and bones of the face. To fill a scientific gap in the field, a novel data set based on extra-oral X-ray images, presenting high variability and with a large number of images, is introduced here. A statistical comparison of the results of 10 pixel-wise image segmentation methods over our proposed data set comprised of 1500 images is also carried out, providing a comprehensive source of performance assessment. Discussion on limitations of the benchmarked methods, as well as future perspectives on exploiting learning-based segmentation methods to improve performance, is also addressed. Finally, we present a preliminary application of the MASK recurrent convolutional neural network to demonstrate the power of a deep learning method to segment images from our data set.

Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives