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Left Ventricle Segmentation in Dynamic 82Rb PET/CT Using Deep Convolutional Neural Networks
Type of publication: Misc
Citation:
Publication status: Accepted
Year: 2025
Month: October
Abstract: Precise delineation of the left ventricle (LV) enables advanced analyses of endo-to-epicardium blood perfusion patterns and their gradients in PET, which can support various clinical applications, including coronary microvascular dysfunction. Approaches like simple thresholding or semi-automatic curve fitting often fall short in accurately capturing the LV boundaries in dynamic $^{82}$Rb PET/CT. They also require expert manual input. This study presents a reliable manual, multi-modal LV delineation strategy, and a fully automatic segmentation algorithm using deep convolutional neural networks (CNNs).
Keywords:
Authors Amirian, Mohammadreza
Chevalley, Arthur
Andrearczyk, Vincent
Klein, Ran
DeKemp, Robert
Moulton, Eric
Kamani, Christel H.
Prior, John O.
Jreige, Mario
Depeursinge, Adrien
Added by: []
Total mark: 0
Attachments
  • EANM25_LV Segmentation.pdf
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