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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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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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Renal Corpuscle01:20

Renal Corpuscle

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The glomerulus and Bowman's capsule are two essential components of the nephron, which is the functional unit of the kidney. These microscopic structures play a critical role in the process of blood filtration to produce urine.
Glomerulus: Structure and Function
The glomerulus is a tiny, intricate network of capillaries located at the beginning of the nephron. It's enveloped by the Bowman's capsule and receives its blood supply from an afferent arteriole, which divides into numerous...
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Internal Anatomy of the Kidney01:12

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The kidneys are essential organs in the human body, performing a myriad of tasks that maintain homeostasis and overall health.
Anatomical Position and Dimensions
The kidneys are retroperitoneal organs positioned against the posterior abdominal wall on either side of the spine, roughly between the twelfth thoracic and third lumbar vertebrae. Each kidney is typically 10-12 cm long, 5-6 cm wide, and 3-4 cm thick, weighing about 150 grams.
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The outermost region of the kidney is the...
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External Anatomy of the Kidney01:21

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The kidneys are a pair of bean-shaped organs in the human body that play a critical role in maintaining overall health. They filter out waste products from the blood, regulate blood pressure, maintain electrolyte balance, and stimulate the production of red blood cells.
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Blood and Nerve Supply to the Kidney01:18

Blood and Nerve Supply to the Kidney

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The kidneys are vital organs responsible for filtering and cleaning blood, removing waste products, and regulating electrolyte levels. To perform these essential functions, they require a constant and robust blood supply.
Bloody Supply to the Kidneys:
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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2.5D MFFAU-Net: a convolutional neural network for kidney segmentation.

Peng Sun1, Zengnan Mo2, Fangrong Hu1

  • 1School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China.

BMC Medical Informatics and Decision Making
|May 10, 2023
PubMed
Summary
This summary is machine-generated.

A novel 2.5D multi-level Feature Fusion Attention U-Net (MFFAU-Net) effectively segments kidney tumors. This deep learning approach achieves high accuracy, offering a valuable tool for clinical practice.

Keywords:
2.5D modelKiTS19KiTS21Kidney tumor segmentationMFFAU-NetMedical image segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Kidney tumors are increasingly prevalent, posing diagnostic challenges due to their heterogeneity.
  • Accurate segmentation is crucial for surgical planning and assessing tumor aggressiveness.
  • Manual segmentation is time-consuming and difficult.

Purpose of the Study:

  • To develop an automated method for segmenting kidneys, tumors, and cysts.
  • To introduce a novel 2.5D deep learning model balancing performance and computational cost.
  • To improve the accuracy of kidney tumor segmentation in medical imaging.

Main Methods:

  • Proposed a 2.5D multi-level Feature Fusion Attention U-Net (MFFAU-Net) architecture.
  • Integrated high-level and low-level features using a ResConv architecture.
  • Utilized multi-level feature fusion for spatial analysis between slices.

Main Results:

  • Achieved an average Dice score of 0.924 on the KiTS19 dataset.
  • Obtained an average Dice score of 0.875 and Surface Dice score of 0.794 on the KiTS21 dataset.
  • Demonstrated comparable performance to high-performance 3D Convolutional Neural Network (CNN) models.

Conclusions:

  • The 2.5D MFFAU-Net model effectively segments kidney tumors.
  • The model shows potential as a clinical reference tool for kidney tumor analysis.
  • This approach offers an efficient alternative to complex 3D models for segmentation.