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Related Concept Videos

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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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.
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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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Related Experiment Video

Updated: Sep 23, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in

Chiu-Han Hsiao1, Ping-Cherng Lin1, Li-An Chung1

  • 1Research Center for Information Technology Innovation, Academia Sinica, Taipei City, (R.O.C.) Taiwan.

Computer Methods and Programs in Biomedicine
|May 14, 2022
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Summary

This study introduces an AI model for precise kidney segmentation in CT scans, achieving a 0.969 Dice score. This advanced kidney segmentation aids surgical planning and disease diagnosis.

Keywords:
Computed tomographyEfficientNetFeature pyramid networkKidney segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Accurate kidney segmentation is crucial for clinical applications like surgical planning and disease assessment.
  • Existing segmentation methods may face challenges with variations in kidney size, shape, and surrounding tissues.

Purpose of the Study:

  • To develop and evaluate a novel encoder-decoder architecture for automated kidney and kidney tumor segmentation in CT images.
  • To optimize the model's performance through rigorous hyperparameter tuning and data augmentation.

Main Methods:

  • An encoder-decoder architecture utilizing EfficientNet-B5 and a feature pyramid network was designed.
  • Hyperparameter optimization included windowing methods, loss functions, and data augmentation strategies.
  • Model evaluation employed five-fold cross-validation on the 2019 Kidney and Kidney Tumor Segmentation Challenge and 3D-IRCAD-01 datasets.

Main Results:

  • The proposed model achieved a high Dice score of 0.969 on the validation dataset.
  • The model demonstrated robust performance across different voxel spacings, anatomical planes, and kidney volumes.
  • Analysis of segmentation outliers and case studies were conducted to ensure reliability.

Conclusions:

  • The developed AI-driven kidney segmentation approach offers significant potential for clinical applications.
  • The model can assist surgeons with pre-operative planning and aid in estimating kidney function for conditions like ADPKD.
  • This technology supports radiologists and clinicians in disease diagnosis and monitoring disease progression.