Identification of dental root canals and their medial line from micro-CT and cone-beam CT records

Abstract

BackgroundShape of the dental root canal is highly patient specific. Automated identification methods of the medial line of dental root canals and the reproduction of their 3D shape can be beneficial for planning endodontic interventions as severely curved root canals or multi-rooted teeth may pose treatment challenges. Accurate shape information of the root canals may also be used by manufacturers of endodontic instruments in order to make more efficient clinical tools.MethodNovel image processing procedures dedicated to the automated detection of the medial axis of the root canal from dental micro-CT and cone-beam CT records are developed. For micro-CT, the 3D model of the root canal is built up from several hundred parallel cross sections, using image enhancement, histogram based fuzzy c-means clustering, center point detection in the segmented slice, three dimensional inner surface reconstruction, and potential field driven curve skeleton extraction in three dimensions. Cone-beam CT records are processed with image enhancement filters and fuzzy chain based regional segmentation, followed by the reconstruction of the root canal surface and detecting its skeleton via a mesh contraction algorithm.ResultsThe proposed medial line identification and root canal detection algorithms are validated on clinical data sets. 25 micro-CT and 36 cone-beam-CT records are used in the validation procedure. The overall success rate of the automatic dental root canal identification was about 92% in both procedures. The algorithms proved to be accurate enough for endodontic therapy planning.ConclusionsAccurate medial line identification and shape detection algorithms of dental root canal have been developed. Different procedures are defined for micro-CT and cone-beam CT records. The automated execution of the subsequent processing steps allows easy application of the algorithms in the dental care. The output data of the image processing procedures is suitable for mathematical modeling of the central line. The proposed methods can help automate the preparation and design of several kinds of endodontic interventions.

Root Canal Morphology of Chalcolithic and Early Bronze Age Human Populations of El Mirador Cave (Sierra de Atapuerca, Spain)

“…However, with the exception of radiography, these techniques destroy the specimen by grinding or splitting it, or preclude further use of the specimen, as with tooth clearing and dye assessments. As alternative techniques to conventional radiography, microcomputed tomography (microCT) and limited cone‐beam computer tomography (CBCT) have been increasingly used in recent years for in‐depth studies of root canal anatomy and have been reported to be more accurate in diagnosing apical radiolucencies than digital X‐rays (Patel, ; Kupczik and Hublin, ; Abella et al, ; Benyó, ; Prado‐Simón et al, ). MicroCT provides high accuracy in imaging internal dental structures, though analysis of archaeological specimens is sometimes limited by the camera size.…”

The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review

“…The 50 studies reporting applications of AI models to analyze 2D/3D images in the dental and maxillofacial region covered a period from November 1992 to January 2019 ( Figure 2). In these studies, six [27][28][29][30][31][32] used periapical radiographs,14 [33][34][35][36][37][38][39][40][41][42][43][44][45][46] used panoramic radiographs, one 47 used both intraoral and panoramic radiographs, ten [48][49][50][51][52][53][54][55][56][57] The Use and Performance of AI Applications in DMFR Hung et al used cephalometric radiographs, 14 [58][59][60][61][62][63][64][65][66][67][68][69][70][71] used CBCT images, one 72 used intraoral photographs, one…”

“…With regard to the applications of these AI models, 19 [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]75 reported on localization/measurement of cephalometric landmarks, nine [35][36][37][38][39][40][41][42][43] on diagnosis of osteoporosis, six 33,34,[67][68][69]76 on classification/segmentation of maxillofacial cysts and/or tumors, three [27][28][29] on identification of alveolar bone resorption, three 30,31,66 on classification of periapical lesions, two 47,74 on the diagnosis of multiple dental diseases, and two 46,72 on classification of tooth types. Furthermore, single studies were found for the identification of root canals, 71 diagnosis of the maxillary sinusitis, 45 identification of inflamed gingiva, 73 identification of dental plaque, 70 detection of den...…”

An Accurate Skeleton Extraction Approach From 3D Point Clouds of Maize Plants

“…The point cloud of trees without leaves are featured as cylindrical branched structure, which are suitable for skeleton extraction ( Livny et al, 2010 ; Delagrange et al, 2014 ; Wang et al, 2014 ). Laplacian skeleton extraction ( Au et al, 2008 ) from point clouds is another widely used method, which has been successfully used in performing skeleton extraction without the need for mesh reconstruction ( Su et al, 2011 ). Extracted curves from point clouds of long and narrow leaves using these methods directly are unsatisfactory, and could not meet the needs of accurate phenotyping traits analysis ( Zhang et al, 2017 ).…”