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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Dental arch definition in computed tomographs using two semi-automatic methods.

Larissa Aparecida Vaz Oliveira1, Maira Beatriz Hernandez Moran2,3, Marcelo Daniel Brito Faria1,4

  • 1Núcleo de Radiologia Odontológica, Policlinica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Avenida Marechal Rondon, 381, Rio de Janeiro, RJ, 20950-003, Brazil.

Medical & Biological Engineering & Computing
|October 11, 2022
PubMed
Summary

Two new semi-automatic methods define dental arches in computed tomography (CT) scans with minimal user input. The mandible-based method (M2) shows high performance, offering a promising auxiliary tool for dental imaging analysis.

Keywords:
Computer programsDental archSoftware toolsTomography

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Area of Science:

  • Dental Imaging
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • Computed tomography (CT) is crucial in dental imaging for precise anatomical localization.
  • The dental arch is a key anatomical feature requiring accurate definition.
  • Semi-automatic methods can reduce user effort in dental image analysis.

Purpose of the Study:

  • To propose two novel semi-automatic methods for dental arch definition in CT scans.
  • To evaluate the performance of these methods with minimal user interaction.
  • To assess the potential of these methods as auxiliary tools in dental imaging.

Main Methods:

  • Development of two semi-automatic algorithms for dental arch segmentation.
  • Method 1 (M1) utilizes teeth pulps; Method 2 (M2) uses the whole mandible.
  • Image processing techniques including thresholding and morphological operations were employed.

Main Results:

  • Initial results for the pulp-based method (M1) were low across evaluation metrics (DTW, IoU).
  • The mandible-based method (M2) demonstrated high average performance.
  • Modifying the input data significantly improved the performance of M1.

Conclusions:

  • The mandible-based semi-automatic method (M2) shows significant potential for accurate dental arch definition.
  • The proposed methods can serve as valuable auxiliary tools in dental CT analysis.
  • Further refinement of input data can enhance the efficacy of pulp-based segmentation approaches.