Trainable WEKA (Waikato Environment for Knowledge Analysis) Segmentation Tool: Machine-Learning-Enabled Segmentation on Features of Panoramic Radiographs

Segmentation of dental radiographs is a comprehensive subject in oral care and diagnosis. It is the process of delineating anatomical structures to simplify the diagnostic process for oral and maxillofacial radiologists. This paper will provide an in-depth analysis of the latest benchmarks in oral imaging by studying the segmentation of panoramic radiographs using Trainable WEKA (Waikato Environment for Knowledge Analysis) Segmentation (TWS). The aim of this research is to accurately automate segmentation where it can be implemented on a large scale of clients in order to simplify radiological diagnosis. The experimentation was conducted by modifying open-source radiographs from UFBA UESC DENTAL IMAGES dataset. In order to simulate realistic conditions such as noise affecting regions of interest, panoramic radiographs were degraded and blurred with Gaussian noise. Accuracy was quantified by measuring the difference between the automated image and the dentist-annotated image using MorphoLibJ. To ensure the precision in results, automated predicted segmentations were observed by an oral maxillofacial radiologist and compared with the dentist-renditioning annotations of the panoramic radiographs (orthopantomograms). The TWS classifier on radiographs with an average of 32 teeth and greater (Dice value of 0.66) and an average of less than 32 teeth (F1 score of 0.59) was significant. The calculated t-value for the Jaccard index is 2.78 and the t-value for the Dice score is 2.81. The results, considering the statistical scores, were due to the independent variable. The radiographs with 32 teeth and greater had higher Intersection over Union scores and F1 scores because of less discrepancy in tooth alignment. Segmentation of dental radiographs can be conducted by machine learning instead of manual segmentation.