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Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks

Medicine and Health

Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks

E. Erdil, A. S. Becker, et al.

This innovative research led by a team of experts—including Ertunc Erdil, Anton S. Becker, and others—introduces a groundbreaking method using convolutional neural networks to enhance the identification of active Brown Adipose Tissue. With results showing a significant boost in accuracy over traditional methods, this study paves the way for more efficient and cost-effective large-scale BAT imaging using unenhanced CT scans.

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Playback language: English
Abstract
Identifying active Brown Adipose Tissue (BAT) typically uses costly and radiation-exposing [¹⁸F]-FDG PET/CT imaging. This study proposes using convolutional neural networks (CNNs) to predict [¹⁸F]-FDG uptake from unenhanced CT scans. Experiments on four cohorts showed that CNNs, particularly the Attention U-Net, achieved 23% to 40% better BAT segmentation accuracy than conventional CT thresholding. BAT volumes from segmentations distinguished subjects with and without active BAT (AUC 0.8 vs 0.6 for CT thresholding). This suggests CNNs can improve large-scale, cost-effective BAT imaging studies using only CT.
Publisher
Nature Communications
Published On
Sep 27, 2024
Authors
Ertunc Erdil, Anton S. Becker, Moritz Schwyzer, Borja Martinez-Tellez, Jonatan R. Ruiz, Thomas Sartoretti, H. Alberto Vargas, A. Irene Burger, Alin Chirindel, Damian Wild, Nicola Zamboni, Bart Deplancke, Vincent Gardeux, Claudia Irene Maushart, Matthias Johannes Betz, Christian Wolfrum, Ender Konukoglu
Tags
Brown Adipose Tissue
CNN
CT scans
imaging
segmentation
radiology
machine learning

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