Rui Wang, Katelyn Craft, Elisa Holtzman, Hannah Mason, Christopher Khan, Brett Byram, Jason Mitchell, and Jack H. Noble. “Evaluation of U-Nets for Object Segmentation in Ultrasound Images.” Proceedings of SPIE Medical Imaging 2024: Ultrasonic Imaging and Tomography, vol. 12932, 129321G, 2024, San Diego, California, United States.
Ultrasound imaging is widely used in medicine due to its safety and cost-effectiveness compared to other methods. However, its quality can vary depending on tissue properties and depth. In this study, researchers tested deep learning techniques to create 3D models of objects imaged with ultrasound. They used three versions of the 3D U-Net model, each trained with different scenarios. The models performed well on specific categories of objects they were trained on but struggled with new categories. Researchers also looked into dual-task autoencoding to improve performance across different object types. These findings set a foundation for further improving the U-Net model to handle a broader range of ultrasound imaging tasks, potentially enhancing visualization and accuracy in medical applications.
