Speaker
Description
Deep learning, particularly employing the Unet architecture, has become pivotal in cardiology, facilitating detailed analysis of heart anatomy and function. The segmentation of cardiac images enables the quantification of essential parameters such as myocardial viability, ejection fraction, cardiac chamber volumes, and morphological features. These segmentation methods operate autonomously with minimal user intervention. Challenges arise in distinguishing the right ventricle from structures like the pulmonary artery, atrium, and aorta at their base, complicating accurate segmentation. To address these challenges, fully convolutional network models have been developed and implemented, optimizing learning parameters. Deep learning approaches for cardiac image segmentation demonstrate promising levels of accuracy. Comprehensively assesses four variants of the Unet architecture, Attention-Unet, TransUnet, U2Net, and Unet++, precisely for the segmentation of cardiac MRI. Utilising these include the Dice Coefficient, IoU Coefficient, Accuracy, and Loss. The analysis focuses on identifying architectural modifications and resource-efficient models that enhance performance. The findings contribute empirical evidence and credibility to inform future model selection for segmentation and analysis purposes.
