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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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...
Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
Imaging Studies III: Computed Tomography01:27

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...

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Updated: Jul 1, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Refining COVID-19 Lesion Segmentation in Lung CT Scans Using Swarm Intelligence and Evolutionary Algorithms.

Wafa Gtifa1, Marwa Fradi2, Anis Sakly1

  • 1Laboratory of Automation and Electrical Systems and Environment, Monastir National School of Engineers (ENIM), University of Monastir, Monastir, Tunisia.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS
|February 7, 2025
PubMed
Summary
This summary is machine-generated.

Swarm intelligence algorithms effectively segment lung lesions in Computed Tomography (CT) scans for COVID-19. Particle Swarm Optimization achieved the highest accuracy, improving diagnostic capabilities.

Keywords:
BFOACOVID‐19GAGSAPSOcomputed tomographysegmentationswarm intelligence algorithms

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Accurate identification of lung lesions in Computed Tomography (CT) scans is critical for managing Coronavirus Disease 2019 (COVID-19).
  • Swarm intelligence algorithms show potential for automated lesion segmentation in medical imaging.

Purpose of the Study:

  • To evaluate and compare the performance of four swarm intelligence algorithms for segmenting COVID-19 related lung lesions in CT scans.
  • To determine the most effective algorithm for improving diagnostic accuracy in COVID-19 detection.

Main Methods:

  • The study implemented and compared four swarm intelligence algorithms: Gravitational Search Algorithm (GSA), Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO).
  • These algorithms were applied to segment lung lesions indicative of COVID-19 in CT images.

Main Results:

  • Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), and Bacterial Foraging Optimization Algorithm (BFOA) demonstrated segmentation accuracies above 90.5%.
  • Particle Swarm Optimization (PSO) achieved the highest segmentation accuracy at 91.45% with an F1 score of 95.54%.
  • The overall segmentation accuracy reached up to 99% using the optimized swarm intelligence approach.

Conclusions:

  • Swarm and evolutionary algorithms are effective tools for segmenting COVID-19 lung lesions.
  • The study highlights the potential of these algorithms to enhance diagnostic accuracy and treatment efficiency for COVID-19 patients.