Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives. (1st October 2018)

• Record Type: Journal Article
• Authors: Silva, Gil; Oliveira, Luciano; Pithon, Matheus
• Is Part Of: Expert systems with applications. Volume 107(2018)
• Page Start: 15
• Page End: 31
• Publication Date: 2018-10-01
• Subjects: Image segmentation -- Dental X-ray -- Orthopantomography; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.04.001
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel data set, which will be available on the acceptance of the paper. Systematic review of the state-of-the-art on tooth segmentation. Benchmark of implemented methods of the state-of-the-art over our data set. Discussion of future perspective in the research field. Abstract: 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.