The XNet deep learning framework achieved a classification accuracy of 98.5% and reduced myocardial area estimation error by 38.6% compared to baseline models for left ventricle quantification.
Does the XNet deep learning framework improve Left Ventricle quantification accuracy compared to baseline models in 2D PET sequences?
The XNet deep learning framework provides highly accurate, automated left ventricular quantification and phase classification from 2D PET images, outperforming baseline models.
Objective: This study aims to improve Left Ventricle (LV) quantification accuracy and efficiency in cardiac disease diagnosis using automated image analysis. Methods: We propose XNet, a deep learning framework based on an extended convolutional network with multi-task learning. XNet segments LV structures estimates Regional Wall Thickness (RWT) and classifies cardiac phases (systole/diastole) from 2D Positron Emission Tomography (PET) sequences. The model integrates spatial and temporal features and is trained on augmented PET datasets. Results: XNet outperformed existing methods, achieving a mean absolute error of 1.5 mm, classification accuracy of 98.5%, validation accuracy of 97.2%, and a loss of 0.048. It showed strong performance in low-contrast and varied-quality image conditions, reducing myocardial area estimation error by 38.6% compared to baseline models. Conclusion: XNet provides a robust, accurate, and fully automated solution for LV quantification, offering a reliable tool to support clinical diagnosis and treatment planning in cardiovascular care.
R et al. (Wed,) conducted a other in Cardiac disease. XNet deep learning framework vs. Baseline models was evaluated on Left Ventricle (LV) quantification accuracy. The XNet deep learning framework achieved a classification accuracy of 98.5% and reduced myocardial area estimation error by 38.6% compared to baseline models for left ventricle quantification.