Co-Evolution of CNNs and Learning Environments for Automated Post-Processing of T2 Parametric Mapping in Cardiovascular Magnetic Resonance

Motivation: Training robust convolutional neural networks (CNNs) for cardiac magnetic resonance (CMR) imaging is essential, as varying imaging protocols and patient demographics can significantly affect performance. Goal(s): The aim is to develop an evolutionary algorithm that computes learning environments (LEs) for CNNs, enhancing their adaptability for myocardial segmentation and reference point estimation in T2 parametric mapping. Approach: The algorithm trains 48 CNNs simultaneously, adjusting LEs through mutation based on performance evaluations. Results: Performance improved over time, achieving a Dice score of 86% and a reference point distance of 6mm, indicating effective adaptability across varying cardiac geometries and pixel intensities. Impact: The evolutionary algorithm improves CNN performance in cardiovascular MRI by dynamically optimizing learning environments, enhancing adaptability across varied imaging conditions. This approach strengthens model generalizability and reliability, making it a promising method for advancing robust AI tools in clinical practice.