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Related Concept Videos

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...
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...

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A Comparative Study of Deep Learning Methods for Multi-Class Semantic Segmentation of 2D Kidney Ultrasound Images.

Simao Valente, Pedro Morais, Helena R Torres

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    Deep learning methods, particularly DeepLabV3+, show promise for accurate kidney segmentation in ultrasound images, outperforming human variability. This advancement aids in diagnosing kidney conditions and improving patient outcomes.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Nephrology

    Background:

    • Accurate kidney segmentation in ultrasound (US) images is crucial for diagnosing and monitoring kidney conditions.
    • Current deep learning methods often neglect internal kidney structures and lack standardized evaluation datasets.
    • Comparative analysis is needed to identify optimal deep learning strategies for kidney segmentation.

    Purpose of the Study:

    • To conduct a comparative analysis of seven deep learning networks for segmenting kidneys and their internal structures in 2D US images.
    • To evaluate network performance on an open-access, multi-class kidney US dataset with inter-expert annotations.
    • To determine the most effective deep learning model for kidney and internal structure segmentation.

    Main Methods:

    • Utilized a multi-class kidney US dataset comprising 321 images with complete annotations for Capsule, Central Echogenic Complex (CEC), Cortex, and Medulla.
    • Compared the performance of seven deep learning networks, including DeepLabV3+.
    • Evaluated segmentation accuracy using the Dice score, comparing results against inter-rater variability.

    Main Results:

    • DeepLabV3+ achieved the highest overall Dice score of 78.0%, surpassing inter-rater variability (75.6%).
    • DeepLabV3+ demonstrated strong performance for specific structures: Capsule (94.2%), CEC (85.8%), Cortex (62.4%), and Medulla (69.6%).
    • The study highlights DeepLabV3+ as a superior model for detailed kidney segmentation in US imaging.

    Conclusions:

    • Deep learning, specifically DeepLabV3+, offers a powerful approach for accurate kidney and internal structure segmentation in US images.
    • This improved segmentation accuracy can enhance diagnostic efficiency and enable new computer-aided applications in nephrology.
    • The findings suggest potential for improved patient outcomes and reduced healthcare costs through advanced AI in medical imaging.