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Related Concept Videos

Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Ultrasonography01:17

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Ultrasound volume projection image quality selection by ranking from convolutional RankNet.

Juan Lyu1, Sai Ho Ling2, S Banerjee2

  • 1College of Information and Communication Engineering, Harbin Engineering University, Harbin, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|January 21, 2021
PubMed
Summary
This summary is machine-generated.

Selecting the best 3D ultrasound images for scoliosis assessment is crucial. A novel convolutional RankNet system automatically ranks image quality, achieving over 95.5% accuracy, surpassing human expert performance.

Keywords:
3D ultrasound imagingConvolutional RankNetImage selectionScoliosis

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Area of Science:

  • Medical Imaging
  • Orthopedics
  • Artificial Intelligence

Background:

  • Scoliosis assessment requires periodic inspection and evaluation.
  • 3D ultrasound imaging offers a real-time, cost-effective, and radiation-free alternative for scoliosis assessment.
  • Generating 3D ultrasound spine images produces multiple 2D coronal slices of varying quality, necessitating selection of the best image for accurate measurement.

Purpose of the Study:

  • To develop an automated method for selecting the highest quality 2D ultrasound images for scoliosis measurement.
  • To improve the accuracy and efficiency of scoliosis assessment using 3D ultrasound data.

Main Methods:

  • A convolutional neural network (CNN) was employed as the backbone for efficient feature extraction and enhanced discriminative ability.
  • A pairwise learning-to-rank network, RankNet, was utilized for image quality ranking.
  • The proposed convolutional RankNet model was trained and evaluated by inputting image pairs to determine optimal image selection based on ranking orders.

Main Results:

  • The convolutional RankNet model achieved a top-3 accuracy exceeding 95.5% in selecting the best quality ultrasound images.
  • The automated system demonstrated performance superior to that of human experts in distinguishing and ranking image quality.
  • The method effectively addresses the challenge of distinguishing subtle quality differences between adjacent ultrasound images.

Conclusions:

  • The developed convolutional RankNet system provides an effective and accurate automated solution for selecting optimal 2D ultrasound images in scoliosis assessment.
  • This AI-driven approach enhances the reliability and efficiency of 3D ultrasound imaging for scoliosis diagnosis and monitoring.
  • The findings suggest a significant advancement in medical imaging analysis for orthopedic conditions, potentially improving patient outcomes.