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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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External Anatomy of the Kidney01:21

External Anatomy of the Kidney

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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.
The kidneys are located in the retroperitoneal space on either side of the vertebral column, protected posteriorly by the 11th and 12th ribs. The right kidney sits slightly lower than the left owing to the presence of the liver...
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Internal Anatomy of the Kidney01:12

Internal Anatomy of the Kidney

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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.
Renal Cortex
The outermost region of the kidney is the...
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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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Renal Tubule and Collecting Duct01:24

Renal Tubule and Collecting Duct

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The renal tubule is divided into three parts: the proximal convoluted tubule (PCT), the Loop of Henle (LOH), and the distal convoluted tubule (DCT).
Proximal Convoluted Tubule (PCT):
The PCT is the initial segment of the renal tubule, extending from the Bowman's capsule that encloses the glomerulus. Its convoluted structure and microvilli-lined cells increase the surface area for reabsorption. The PCT reabsorbs glucose, amino acids, sodium, and water from the filtrate, ensuring essential...
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DiagNeXt: A Two-Stage Attention-Guided ConvNeXt Framework for Kidney Pathology Segmentation and Classification.

Hilal Tekin1, Şafak Kılıç2,3, Yahya Doğan4

  • 1Department of Computer Engineering, Gaziantep Islamic Science and Technology University, Gaziantep 27260, Turkey.

Journal of Imaging
|December 24, 2025
PubMed
Summary

DiagNeXt, a deep learning framework, accurately segments and classifies kidney pathologies. This novel approach significantly improves diagnostic accuracy and offers interpretable uncertainty maps for better clinical decisions.

Keywords:
ConvNeXtattention mechanismsdeep learningkidney pathologymedical image segmentationtwo-stage framework

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Nephrology Diagnostics

Background:

  • Accurate kidney pathology segmentation and classification are challenging due to morphological variations and class imbalance.
  • Existing computer-aided diagnosis methods struggle with complex kidney image analysis.

Purpose of the Study:

  • To introduce DiagNeXt, a two-stage deep learning framework for enhanced kidney pathology segmentation and classification.
  • To address challenges in medical image analysis for computer-aided diagnosis of kidney diseases.

Main Methods:

  • Developed DiagNeXt, a two-stage framework using attention-enhanced ConvNeXt architectures for segmentation (DiagNeXt-Seg) and classification (DiagNeXt-Cls).
  • Incorporated Enhanced Convolutional Blocks (ECBs), spatial attention, Atrous Spatial Pyramid Pooling (ASPP), Context-Aware Feature Fusion (CAFF), and Evidential Deep Learning (EDL).
  • Utilized a boundary-aware compound loss and attention-guided skip connections for precise segmentation and feature preservation.

Main Results:

  • Achieved 98.9% classification accuracy, surpassing state-of-the-art by 6.8% on a large kidney CT dataset.
  • Demonstrated near-perfect AUC scores for Normal (1.000), Tumor (1.000), Cyst (0.999), and Stone (0.994) pathologies.
  • Showcased 6.2× faster inference speed and provided clinically interpretable uncertainty maps and attention visualizations.

Conclusions:

  • DiagNeXt offers superior diagnostic accuracy and computational efficiency for kidney pathology analysis.
  • The framework's interpretability and uncertainty estimation enhance its clinical applicability.
  • DiagNeXt shows strong potential for integration into clinical systems for kidney disease diagnosis and treatment planning.