Left Ventricle Segmentation in Dynamic 82Rb PET/CT Using Deep Convolutional Neural Networks
| Art der Publikation: | beliebiger Eintrag |
| Zitat: | |
| Publication status: | Accepted |
| Jahr: | 2025 |
| Monat: | Oktober |
| Abriss: | 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). |
| Schlagworte: | |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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