Building a GUI Tool for Automated Aortic Segmentation in Low-Dose Chest CT Images with PET-Based Standard Uptake Value (SUV) Analysis.

Georgios-Eleftherios Kalykakis, Panagiotis Siogkas, Constantinos Anagnostopoulos, Themistoklis Exarchos

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Article in Advances in experimental medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Georgios-Eleftherios KalykakisBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Kerkyra, Greece.

Panagiotis SiogkasDepartment of Biomedical Technology, University of Ioannina, Ioannina, Greece.

Constantinos AnagnostopoulosBiomedical Research Foundation of the Academy of Athens, Athens, Greece.

Themistoklis ExarchosBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Kerkyra, Greece.

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6 · The paper itself

Abstract

introductionThe use of PET-CT combined with Fluorodeoxyglucose (FDG) has enhanced the detection and management of conditions like vasculitis and lymphoma. However, the current approach to measuring standardized uptake value (SUV) requires manual selection of Regions of Interest (ROI), which is time-intensive. This study introduces an automated method to measure tracer uptake in the aorta, aiming to improve efficiency and accuracy.

methodsWe conducted a proof-of-concept study on PET-CT scans, including those with vasculitis, lymphoma, and healthy controls. A UNET model with a ResNet-18 backbone was used for automated aorta segmentation and SUVmax calculation. We assessed the model's performance using the Intersection over Union (IoU) and compared SUVmax results from automated and manual methods.

resultsThe model achieved an IoU score of 0.905 in the validation slides set, reflecting strong segmentation performance. The mean SUVmax between automated and manual methods showed no significant difference (2.75 vs 2.28, p = 0.38). The automated process significantly reduced segmentation time from 36.3 minutes to 4.8 minutes.

conclusionsAutomated aortic segmentation and SUV calculation using deep learning techniques can enhance PET-CT diagnostic workflows by matching the accuracy of manual methods while greatly reducing processing time.

Indexed as

AortaLymphomaPositron Emission Tomography Computed TomographyAgedFemaleFluorodeoxyglucose F18HumansImage Processing, Computer-AssistedMaleMiddle AgedRadiopharmaceuticalsFluorodeoxyglucose F18RadiopharmaceuticalsAutomated aorta segmentationDeep learningMeasurement of tracer uptake in the aortaPET-CT diagnostic workflowsStandardized uptake value calculation

Identifiers

PMID41252016

DOI10.1016/j.neuroimage.2006.01.015

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