A Pre-Trained Model Customization Framework for Accelerated PET/MR Segmentation of Abdominal Fat in Obstructive Sleep Apnea

<b>Background</b>: Accurate quantification of visceral (VAT) and subcutaneous adipose tissue (SAT) is critical for understanding the cardiometabolic consequences of obstructive sleep apnea (OSA) and other chronic diseases. This study validates a customization framework using pre-trained networks for...

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Bibliografische gegevens
Hoofdauteurs:	Valentin Fauveau, Heli Patel, Jennifer Prevot, Bolong Xu, Oren Cohen, Samira Khan, Philip M. Robson, Zahi A. Fayad, Christoph Lippert, Hayit Greenspan, Neomi Shah, Vaishnavi Kundel
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	MDPI AG 2025-12-01
Reeks:	Diagnostics
Onderwerpen:	obstructive sleep apnea (OSA) visceral adipose tissue (VAT) subcutaneous adipose tissue (SAT) cardiovascular disease (CVD) artificial intelligence (AI) deep learning (DL)
Online toegang:	https://www.mdpi.com/2075-4418/15/24/3243
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A Pre-Trained Model Customization Framework for Accelerated PET/MR Segmentation of Abdominal Fat in Obstructive Sleep Apnea

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