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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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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Ensemble learning and tensor regularization for cone-beam computed tomography-based pelvic organ segmentation.

Hanyue Zhou1, Minsong Cao2, Yugang Min2

  • 1Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, USA.

Medical Physics
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an ensemble deep-learning model for segmenting organs after prostatectomy using low-dose cone-beam computed tomography (CBCT). The model improves organ contour accuracy and robustness for better radiotherapy planning.

Keywords:
CTcone-beam CTdeep learningpelvic segmentationtensor regularization

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Cone-beam computed tomography (CBCT) offers low-dose imaging for radiotherapy but suffers from low contrast and artifacts, hindering accurate organ segmentation.
  • Accurate segmentation of organs at risk is crucial for effective radiotherapy planning and minimizing side effects.

Purpose of the Study:

  • To develop an automated ensemble deep-learning model for segmenting post-prostatectomy organs on CBCT images.
  • To overcome the limitations of low image quality in CBCT for precise organ boundary identification.

Main Methods:

  • An ensemble deep-learning approach was employed, integrating semantic attention from a You-only-look-once detector and multiple 2.5D segmentation networks.
  • Auxiliary high-quality CT data was used to enhance CBCT segmentation.
  • A novel tensor-regularized ensemble scheme aggregated multi-view estimates and ensured spatial integrity of the final segmentation.

Main Results:

  • The model achieved a Dice similarity coefficient of 0.779 for the rectum and 0.915 for the bladder.
  • Mean surface distances were 2.895 mm for the rectum and 1.675 mm for the bladder.
  • The ensemble scheme demonstrated robustness and improved geometric integrity of organ contours.

Conclusions:

  • The proposed ensemble deep-learning model effectively enhances the geometric integrity and robustness of organ contours from CBCT.
  • Tensor regularization ensures anatomically plausible segmentation results, supporting clinical interpretation and decision-making in radiotherapy.