%Aigaion2 BibTeX export von HES SO Valais Publications
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@MISC{,
    author = {Amirian, Mohammadreza and Chevalley, Arthur and Andrearczyk, Vincent and Klein, Ran and DeKemp, Robert and Moulton, Eric and Kamani, Christel H. and Prior, John O. and Jreige, Mario and Depeursinge, Adrien},
     month = oct,
     title = {Left Ventricle Segmentation in Dynamic 82Rb PET/CT Using Deep Convolutional Neural Networks},
      year = {2025},
  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).}
}