Elevating semantic segmentation: a conditional generative adversarial network (CGAN)-based synthetic scene image generation for enhanced precision

Accurately segmenting the left ventricle in end-diastolic echocardiography images is vital for precise cardiac diagnosis. Existing methods often struggle with limited training data, impacting segmentation accuracy. This study addresses this challenge by proposing a novel conditional generative adversarial network (CGAN) approach. Building on the U-Net architecture, the CGAN generates synthetic scene images resembling real echocardiography data, augmenting the training dataset. We compare our CGAN-based model against five cutting-edge segmentation models. The experiments demonstrated that the proposed CGAN-based approach achieved an overall accuracy improvement of 0.12 in left ventricle segmentation, therefore overcoming limitations imposed by small datasets. This breakthrough contributes to medical image segmentation, showcasing the potential of CGANs for enhancing precision in crucial cardiac assessments. Our innovative methodology paves the way for further advancements in analyzing echocardiography images, ultimately improving the quality of cardiac diagnostics.