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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: Apr 18, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
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Published on: April 9, 2019

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Automated consensus contour building for prostate MRI.

Farzad Khalvati

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an algorithm to automatically create consensus contours from multiple clinician atlases, reducing uncertainty in radiation therapy planning. The method achieved an 88% median Dice similarity coefficient on prostate MR images.

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    Area of Science:

    • Medical imaging
    • Radiation oncology
    • Computational anatomy

    Background:

    • Inter-observer variability in medical image contouring introduces uncertainty in radiation treatment planning.
    • Consensus contours, generated from multiple clinician inputs, aim to reduce this variability but are costly to produce.
    • Manual contouring of organs and tumors requires significant clinical expertise and time.

    Purpose of the Study:

    • To develop and evaluate an algorithm for automatically generating consensus contours from clinician atlases.
    • To reduce the cost and time associated with creating consensus contours for radiation treatment planning.
    • To assess the accuracy of automatically generated consensus contours compared to manual consensus contours.

    Main Methods:

    • An algorithm was developed to automatically generate a consensus contour using multiple clinician-generated atlases.
    • The algorithm was applied to prostate magnetic resonance (MR) images from 15 patients.
    • Each patient's images were manually contoured by 5 clinicians.

    Main Results:

    • The automatic consensus contours were compared against manual consensus contours.
    • A median Dice similarity coefficient (DSC) of 88% was achieved, indicating high agreement.
    • The algorithm demonstrates feasibility in generating accurate consensus contours.

    Conclusions:

    • Automatic generation of consensus contours using clinician atlases is a viable alternative to manual methods.
    • This approach can significantly reduce the cost and effort in radiation treatment planning.
    • The developed algorithm shows potential for improving consistency and reducing uncertainty in radiotherapy.