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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: Mar 2, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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SU-E-J-101: Weighted Voting Method for Multi-Atlas Segmentation in CT Scans.

A Arbisser1,2, G Sharp1,2, P Golland1,2

  • 1Massachusetts Institute of Technology, Cambridge, MA.

Medical Physics
|May 19, 2017
PubMed
Summary

This study introduces an automated method for segmenting head and neck CT scans, significantly reducing manual contouring time for structures like the brainstem and parotids.

Keywords:
Computed tomographyImage transformsMedical image segmentationMedical imaging

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Anatomy

Background:

  • Manual segmentation of anatomical structures in medical imaging, such as CT scans, is time-consuming and requires specialized expertise.
  • Automating this process can improve efficiency and consistency in clinical workflows.

Purpose of the Study:

  • To develop and evaluate an automated method for segmenting the brainstem and parotid glands in head and neck CT scans.
  • To minimize the time physicians spend on manual contouring of these critical structures.

Main Methods:

  • Utilized an atlas-based approach with labeled images to generate contours for unlabeled target images.
  • Employed multi-resolution translational and affine registration, followed by diffeomorphic demons registration.
  • Implemented a weighted voting method for label fusion, considering intensity differences and boundary distances.

Main Results:

  • Achieved improved segmentation accuracy compared to a majority voting method, as indicated by Dice and Hausdorff metrics.
  • Reported mean Dice scores around 0.7 and mean Hausdorff distances of 2-3mm.
  • Maximum Hausdorff distances were approximately 15mm.

Conclusions:

  • The developed automated segmentation method produces contours closely matching manual segmentations, typically within a few millimeters.
  • This approach has the potential to substantially reduce physician workload by minimizing the need for manual contour modification.