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Related Concept Videos

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

Imaging Studies I: Kidney, Ureter, and Bladder Studies

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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...
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Imaging Studies II: Ultrasonography01:24

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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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Kidney Structure01:45

Kidney Structure

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The kidneys are two large bean-shaped organs located in the upper abdomen. They filter the blood several times a day to remove toxins and rebalance water and electrolytes of the circulatory system via the renal veins. The kidneys receive blood directly from the heart via the renal arteries. These arteries enter the kidney at the hilum, the concave surface of the bean, where they branch and divide into smaller vessels and capillaries.
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Related Experiment Video

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Use of 3D Robotic Ultrasound for In Vivo Analysis of Mouse Kidneys
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Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data.

Tao Peng1,2,3, Yidong Gu4, Shanq-Jang Ruan5

  • 1School of Future Science and Engineering, Soochow University, Suzhou 215006, China.

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|October 28, 2023
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Summary

This study introduces an automated method for segmenting kidney ultrasound images, improving diagnostic accuracy. The novel coarse-to-refined approach significantly enhances kidney segmentation performance compared to existing techniques.

Keywords:
automatic searching polygon trackingdeep fusion learning networkmathematical mapping modelultrasound kidney segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Nephrology

Background:

  • Kidney ultrasound (US) imaging is crucial for diagnosing and managing kidney conditions.
  • Accurate kidney segmentation in US images is vital for precise diagnosis and treatment planning.
  • Manual segmentation is time-consuming and complex, necessitating automated solutions.

Purpose of the Study:

  • To develop and validate a novel automatic method for kidney segmentation in ultrasound images.
  • To improve the accuracy and robustness of kidney segmentation compared to existing methods.
  • To provide a tool that enhances the rigor of kidney US segmentation for clinical applications.

Main Methods:

  • A two-step cascaded approach was developed: coarse segmentation using a deep fusion learning network, followed by refinement with a polygon tracking and machine learning network.
  • The machine learning network utilizes an explainable mathematical formula for kidney contours.
  • The method was evaluated on 1380 trans-abdominal US kidney images from 115 patients.

Main Results:

  • The proposed method achieved a Dice Similarity Coefficient (DSC) of 94.6 ± 3.4%, outperforming state-of-the-art deep learning (89.4 ± 7.1%) and hybrid algorithms (93.7 ± 3.8%).
  • Ablation experiments confirmed the significance of each component in the coarse-to-refined architecture.
  • The method demonstrated accurate and robust kidney segmentation across varying noise levels.

Conclusions:

  • A novel coarse-to-refined architecture enables accurate segmentation of kidney ultrasound images.
  • Precise kidney contour extraction is critical to avoid errors in treatments like US-guided brachytherapy.
  • The developed method offers a significant advancement for improving the accuracy and reliability of kidney US segmentation.