Dataset Information

Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT.

ABSTRACT: High-resolution micro-computed tomography is a powerful tool to analyze and visualize the internal morphology of human permanent teeth. It is increasingly used for investigation of epidemiological questions to provide the dentist with the necessary information required for successful endodontic treatment. The aim of the present paper was to propose an image processing method to automate parts of the work needed to fully describe the internal morphology of human permanent teeth. One hundred and four human teeth were scanned on a high-resolution micro-CT scanner using an automatic specimen changer. Python code in a Jupyter notebook was used to verify and process the scans, prepare the datasets for description of the internal morphology and to measure the apical region of the tooth. The presented method offers an easy, non-destructive, rapid and efficient approach to scan, check and preview tomographic datasets of a large number of teeth. It is a helpful tool for the detailed description and characterization of the internal morphology of human permanent teeth using automated segmentation by means of micro-CT with full reproducibility and high standardization.

SUBMITTER: Haberthur D

PROVIDER: S-EPMC8040229 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT.

Haberthür David D Hlushchuk Ruslan R Wolf Thomas Gerhard TG

BMC oral health 20210412 1

High-resolution micro-computed tomography is a powerful tool to analyze and visualize the internal morphology of human permanent teeth. It is increasingly used for investigation of epidemiological questions to provide the dentist with the necessary information required for successful endodontic treatment. The aim of the present paper was to propose an image processing method to automate parts of the work needed to fully describe the internal morphology of human permanent teeth. One hundred and f ...[more]

PMID: 33845806

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Project description:PurposeIn this work, we address image segmentation in the scope of dosimetry using deep learning and make three main contributions: (a) to extend and optimize the architecture of an existing convolutional neural network (CNN) in order to obtain a fast, robust and accurate computed tomography (CT)-based organ segmentation method for kidneys and livers; (b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; and (c) to evaluate dosimetry results obtained using automated organ segmentation in comparison with manual segmentation done by two independent experts.MethodsWe adapted a performant deep learning approach using CT-images to delineate organ boundaries with sufficiently high accuracy and adequate processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the activity values from quantitatively reconstructed SPECT images for "volumetric"/3D dosimetry. The resulting activities were used to perform dosimetry calculations with the kidneys as source organs.ResultsThe computational expense of the algorithm was sufficient for clinical daily routine, required minimum pre-processing and performed with acceptable accuracy a Dice coefficient of [Formula: see text] for liver segmentation and of [Formula: see text] for kidney segmentation, respectively. In addition, kidney self-absorbed doses calculated using automated segmentation differed by [Formula: see text] from dosimetry performed by two medical physicists in 8 patients.ConclusionThe proposed approach may accelerate volumetric dosimetry of kidneys in molecular radiotherapy with 177Lu-labelled radiopharmaceuticals such as 177Lu-DOTATOC. However, even though a fully automated segmentation methodology based on CT images accelerates organ segmentation and performs with high accuracy, it does not remove the need for supervision and corrections by experts, mostly due to misalignments in the co-registration between SPECT and CT images. Trial registration EudraCT, 2016-001897-13. Registered 26.04.2016, www.clinicaltrialsregister.eu/ctr-search/search?query=2016-001897-13 .

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Project description:BackgroundTo investigate the anatomic features of the root furcation of permanent mandibular first molars.MethodsA total of 50 extracted mandibular first molars (25 two-rooted and 25 three-rooted) were collected and scanned using micro-computed tomography. The digital models of teeth and root canal systems were reconstructed three-dimensionally. The tooth models were displayed in parallel projection mode from buccal and distal views. Screenshots were captured and subsequently analyzed using Image-Pro Plus 6.0 software after calibration. The furcation angle, root trunk length, maximum depth and level of distal root concaves of mesial roots, and length of enamel projections were measured, and the furcation types (classified into type V, type U and type W) were detected. Statistical analysis was performed using the Shapiro-Wilk's test, one-way analysis of variance, Student's t-test and Chi-square test.ResultsThe mean furcation angle between the distobuccal (DB) and distolingual (DL) roots (in distal view) was the greatest (59.2°), whereas the furcation angle between the mesial and DL roots (in buccal view) was the smallest (25.4°) among the four furcation angles (all p < 0.05). Regarding the furcation types, bucco-lingual root trunk length, maximum depth and site of the distal root concavities, and enamel projection length, no significant differences were detected between the three- and two-rooted molar groups (all p > 0.05). The frequency of type V was the highest (54.0%), followed by type U (26.0%), and type W had the lowest occurrence rate (20.0%). The mean length of distal root trunk in the three-rooted mandibular molars was significantly greater than that of the buccal/lingual one (3.7 mm vs. 3.0 mm, p < 0.01). The maximum depth of the distal concavities of the mesial roots was on average 0.66 ± 0.19 mm, and the site was located at an average of 2.8 ± 1.3 mm below furcation. The mean length of buccal enamel projections was significantly longer than that of lingual ones (3.1 mm vs. 0.7 mm, p < 0.01).ConclusionsThe furcation anatomy of the mandibular first molar is complex, and the presence of the DL root may further complicate its topography. A thorough understanding of these anatomic features is essential for successful periodontal treatment.

Dataset Information

Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT.

Publications

Automated segmentation and description of the internal morphology of human permanent teeth by means of micro-CT.

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