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Related Concept Videos

Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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...
Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
Ultrasonography01:17

Ultrasonography

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 a...
Ultrasound I: Abdominal Ultrasonography01:20

Ultrasound I: Abdominal Ultrasonography

Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...

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Related Experiment Video

Updated: Jun 24, 2026

Point-of-Care Kidney and Genitourinary Ultrasound in Adults: Image Acquisition
03:19

Point-of-Care Kidney and Genitourinary Ultrasound in Adults: Image Acquisition

Published on: June 21, 2024

Ultrasound Domain Adaptation for Robust Kidney Segmentation via Spectral-Similarity-Guided Translation.

De Yu1, Jinyan Cai2, Menglin Wu2,3

  • 1School of Information Technology, Jiangsu Open University, Nanjing, China.

Journal of Imaging Informatics in Medicine
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel ultrasound image translation method that uses spectral similarity to improve kidney segmentation accuracy across different devices and centers. The technique enhances model generalization without needing new annotations, significantly reducing segmentation errors.

Keywords:
Diffusion modelsDomain adaptationKidney segmentationSpectral statisticsUltrasound image translation

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Accurate kidney ultrasound segmentation is crucial for diagnosis and measurement but is hindered by domain shifts across devices and centers.
  • Acquiring pixel-level annotations for new domains is time-consuming and expensive, limiting model generalization.
  • Existing methods struggle to adapt to variations in grayscale intensity, contrast, and speckle texture.

Purpose of the Study:

  • To develop a method for improving kidney ultrasound segmentation performance without requiring target-domain annotations.
  • To address the challenge of domain shifts in ultrasound imaging by translating images to a target domain appearance while preserving anatomical structures.
  • To enhance the robustness and generalization of segmentation models in zero-shot and cross-center settings.

Main Methods:

  • A statistical spectral-similarity-guided ultrasound-to-ultrasound translation approach was proposed.
  • The method leverages frequency-domain analysis to identify consistent spectral bands across domains, deriving structural guidance.
  • This guidance is integrated into a diffusion-based image generation process, followed by training a segmentation network with source-domain labels.

Main Results:

  • The proposed method demonstrated superior structural preservation in image translation compared to existing techniques.
  • Consistently improved downstream kidney segmentation performance was observed across multiple datasets, including public and in-house multi-center data.
  • Significant reductions in boundary error were achieved, with a 20.52% increase in mean Dice score and a 71.96 mm reduction in 95% Hausdorff Distance in a challenging adaptation scenario.

Conclusions:

  • The spectral-similarity-based guidance effectively handles ultrasound domain shifts, enhancing segmentation model robustness and generalization.
  • The method provides a cost-effective solution for improving kidney segmentation accuracy in diverse clinical settings without target-domain annotations.
  • This work offers a promising direction for developing more adaptable and reliable AI tools in medical imaging.