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Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

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...

Imaging Studies I: Kidney, Ureter, and Bladder Studies

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: Ultrasonography

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...

Urinary Tract Calculi VI: Surgical Management

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Procedures for Kidney StonesMedical intervention is necessary when kidney stones or renal calculi are too large to pass spontaneously (typically greater than 5 millimeters) when stones are accompanied by symptomatic infection (such as fever or pyelonephritis), when they impair kidney function, or when they cause persistent symptoms like severe pain, nausea, or urinary retention. Additionally, patients with only one kidney or those who cannot be treated with medical management also require...

Urinary Tract Calculi III: Medical Management

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The diagnosis of renal calculi involves several imaging techniques, including non-contrast CT scans and ultrasound. These methods help visualize kidney stones, assess their size and location, and detect possible obstructions. Additionally, Measuring urine pH is useful for diagnosing specific stone types, such as struvite (alkaline pH) and uric acid stones (acidic pH). Cystine stones are primarily linked to cystinuria, a genetic condition. A urinalysis helps detect blood in the urine (hematuria)...

Urinary Tract Calculi I: Introduction

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Renal calculi, or kidney stones, are solid deposits of minerals and salts formed inside the kidneys. In medical terminology, "calculus" refers to the stone itself, while "lithiasis" describes the process of stone formation. Depending on their location within the urinary system, these stones may be classified as either urolithiasis, when situated within the urinary tract, or nephrolithiasis, when located within the kidneys. Each term signifies the specific impact of the stone.Predisposition...

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Related Experiment Video

Updated: Jul 31, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Effective deep learning classification for kidney stone using axial computed tomography (CT) images.

Özlem Sabuncu¹, Bülent Bilgehan¹, Enver Kneebone²

¹Department of Electrical and Electronic Engineering, Near East University, Nicosia, Mersin, Türkiye.

Biomedizinische Technik. Biomedical Engineering

|May 2, 2023

Summary

Deep learning models accurately detect kidney stones in CT scans. The Inception-V3 model achieved 98.52% accuracy, aiding radiologists in clinical diagnosis with reduced computational cost.

Keywords:

artificial intelligence computed tomography convolutional neural network deep learning kidney stone

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Area of Science:

Medical Imaging
Artificial Intelligence
Nephrology

Background:

Kidney stones are a prevalent condition with high recurrence and morbidity rates.
Computed tomography (CT) imaging is the preferred diagnostic method for kidney stones.
Manual analysis of CT scans for kidney stone diagnosis is labor-intensive and time-consuming.

Purpose of the Study:

To accurately classify kidney stones from CT scans using deep learning (DL) algorithms.
To develop an automated system for kidney stone detection, reducing radiologist workload.

Main Methods:

The Inception-V3 model was utilized as a reference architecture.
Deep learning models were pre-trained using Convolutional Neural Network (CNN) architectures.
The models were applied to a dataset of abdominal CT scans from patients with kidney stones.

Main Results:

Eight DL models were evaluated on 8,209 CT images.
The Inception-V3 model demonstrated a high test accuracy of 98.52% in detecting kidney stones.
The study utilized a limited set of authentic CT images for training and testing.

Conclusions:

The Inception-V3 model is effective in detecting small kidney stones.
The model's high performance indicates its suitability for clinical applications.
This DL approach can assist radiologists, reducing computational costs and the need for extensive expert review.