Ultrasound Localization Microscopy using Ensemble Learning

Noninvasive imaging of deep-tissue microvascular structures is essential for accurate clinical diagnosis and monitoring. Ultrasound Localization Microscopy (ULM) achieves subwavelength resolution imaging but is hindered by challenges such as extended acquisition times, high microbubble (MB) concentration requirements, and localization inaccuracies. In this study, we propose ENS-ULM, an ensemble model that integrates a Swin transformer with a subpixel Convolutional Neural Network (CNN) to enhance MB localization in ULM. Using synthetic data, we validated ENS-ULM with metrics including the Jaccard index and localization precision. Our ensemble model outperformed traditional approaches, such as Gaussian Fitting and Radial Symmetry, as well as individual CNN and Swin transformer models, achieving superior imaging precision and accuracy. These findings highlight the potential of ensemble methods in advancing MB localization performance for ULM applications.