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

Imaging Studies I: Kidney, Ureter, and Bladder Studies

31
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...
31
Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

34
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...
34
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

42
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
42
Anatomy of the Genitourinary System I: Kidneys and Ureters01:11

Anatomy of the Genitourinary System I: Kidneys and Ureters

66
The upper urinary system comprises two kidneys and two ureters, which are crucial in filtering blood and forming urine.KidneysLocation and Structure:The kidneys are two bean-shaped organs positioned behind the peritoneum on either side of the spine.Kidneys are between the 12th thoracic (T12) and the 3rd lumbar (L3) vertebrae.The position of the liver causes the right kidney to sit slightly lower than the left.Protective Layers:Each kidney is enveloped in a tough, fibrous membrane called the...
66
Computed Tomography01:10

Computed Tomography

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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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Automatic segmentation of kidneys in computed tomography images using U-Net.

D M Khalal1, H Azizi1, N Maalej2

  • 1Laboratory of dosing, analysis and characterization in high resolution, Department of Physics, Faculty of Sciences, Ferhat Abbas Sétif 1 University, El Baz campus 19137, Sétif, Algeria.

Cancer Radiotherapie : Journal De La Societe Francaise De Radiotherapie Oncologique
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PubMed
Summary

Deep learning accurately segments kidneys on CT scans for radiation therapy planning, improving speed and precision over manual methods. This automated approach enhances organ contouring in radiotherapy.

Keywords:
Automatic segmentationCT imagesImages CTKidneysReinsSegmentation automatiqueU-Net

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Area of Science:

  • Medical Imaging
  • Radiation Oncology
  • Artificial Intelligence

Background:

  • Accurate segmentation of organs at risk in CT images is crucial for radiation therapy planning.
  • Manual segmentation is time-consuming, prone to inter-observer variability, and dependent on clinician experience.
  • Deep learning (DL) offers a potential solution for automated segmentation.

Purpose of the Study:

  • To utilize a DL-based method for segmenting kidneys in CT images.
  • To facilitate radiotherapy treatment planning through automated kidney segmentation.
  • To evaluate the efficacy of DL in improving segmentation accuracy and speed.

Main Methods:

  • CT scans from 20 patients were analyzed.
  • The U-Net model was employed for kidney segmentation.
  • Quantitative evaluation used Dice Similarity Coefficient (DSC), Matthews Correlation Coefficient (MCC), Hausdorff Distance (HD), sensitivity, and specificity.

Main Results:

  • The U-Net model demonstrated good accuracy in segmenting kidneys.
  • Detailed performance metrics for kidney segmentation were obtained and presented.
  • Results were benchmarked against recent findings from other studies.

Conclusions:

  • Fully automated DL-based segmentation of CT images can significantly enhance radiotherapy organ contouring.
  • This approach has the potential to improve both the speed and accuracy of the segmentation process.
  • DL methods represent a promising advancement for radiotherapy treatment planning.