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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Related Experiment Video

Updated: Sep 5, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm.

Shota Ichikawa1,2, Hideki Itadani2, Hiroyuki Sugimori3

  • 1Graduate School of Health Sciences, Hokkaido University, Kita-12, Nishi-5, Kita-ku, Sapporo, 060-0812, Japan.

Physical and Engineering Sciences in Medicine
|July 6, 2022
PubMed
Summary
This summary is machine-generated.

A new system automatically reformats head CT scans using an object detection algorithm, improving consistency and saving time for lesion detection and follow-up. This automated approach offers clinically useful image quality efficiently.

Keywords:
Computed tomographyDeep learning algorithmsObject detectionOrbitomeatal lineYou Look Only Once (YOLO)v5 model

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Consistent cross-sectional imaging is crucial for accurate lesion detection and follow-up in head CT scans.
  • Manual image reformation introduces variability and is time-consuming for technologists.

Purpose of the Study:

  • To develop and evaluate an automated system for reformatting head CT images at the orbitomeatal (OM) line.
  • To assess the performance and clinical utility of this automated reformation system.

Main Methods:

  • A You Look Only Once (YOLO)v5 model was trained to detect four key landmarks (eyes, external auditory canal).
  • Head CT images from 681 patients were reformatted at the OM line using the trained model.
  • System performance was evaluated using precision, recall, mean average precision, and qualitative assessment by radiologists.

Main Results:

  • The YOLOv5 model achieved a mean average precision of 0.827.
  • The automated reformation process took an average of 23.5 seconds per case.
  • Radiologists rated the automated reformation quality as clinically useful (scores 3 or 4) in 86.8% to 94.1% of cases.

Conclusions:

  • The developed system effectively reformats head CT images at the OM line using an object detection algorithm.
  • The automated system demonstrates acceptable image quality and significant time efficiency for clinical use.