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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

248
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...
248

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Related Experiment Video

Updated: Jan 6, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
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Building a GUI Tool for Automated Aortic Segmentation in Low-Dose Chest CT Images with PET-Based Standard Uptake

Georgios-Eleftherios Kalykakis1,2, Panagiotis Siogkas3, Constantinos Anagnostopoulos2

  • 1Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Kerkyra, Greece.

Advances in Experimental Medicine and Biology
|November 18, 2025
PubMed
Summary

An automated method for measuring tracer uptake in the aorta using PET-CT significantly reduces processing time compared to manual methods. This deep learning approach enhances diagnostic workflows by maintaining accuracy while improving efficiency for conditions like vasculitis and lymphoma.

Keywords:
Automated aorta segmentationDeep learningMeasurement of tracer uptake in the aortaPET-CT diagnostic workflowsStandardized uptake value calculation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Positron Emission Tomography-Computed Tomography (PET-CT) with Fluorodeoxyglucose (FDG) is crucial for diagnosing conditions like vasculitis and lymphoma.
  • Current Standardized Uptake Value (SUV) measurements require manual Region of Interest (ROI) selection, which is time-consuming.

Purpose of the Study:

  • To develop and validate an automated method for aorta segmentation and SUVmax calculation in PET-CT scans.
  • To improve the efficiency and accuracy of PET-CT analysis for oncological and inflammatory conditions.

Main Methods:

  • A UNET model with a ResNet-18 backbone was employed for automated aorta segmentation.
  • The model's performance was evaluated using Intersection over Union (IoU) and compared against manual SUVmax measurements.
  • The study included PET-CT scans from patients with vasculitis, lymphoma, and healthy controls.

Main Results:

  • The automated segmentation achieved a high IoU score of 0.905 on the validation set.
  • No significant difference was observed in mean SUVmax between automated (2.75) and manual (2.28) methods (p=0.38).
  • Automated segmentation reduced processing time from 36.3 minutes to 4.8 minutes.

Conclusions:

  • Deep learning-based automated aortic segmentation and SUV calculation enhance PET-CT workflows.
  • The automated method provides comparable accuracy to manual techniques with substantial time savings.
  • This approach offers a more efficient diagnostic tool for PET-CT imaging.