Guest Editorial: Deep Learning in Ultrasound Imaging

Among the different imaging modalities, ultrasound is the most widespread modality for visualizing human tissue due to it being low-cost, non-ionizing, real-time with immediate feedback to the sonographer, convenient to operate, widely available and well established, with a very large number of images generated in a single setting. On the other hand, ultrasound imaging suffers from the disadvantage of being user dependent and of variable quality,which makes the automated interpretation of ultrasound images often very difficult. In recent years, algorithms in medical imaging have been significantly improved thanks to the advent of deep learning methods (including convolutional neural networks, recurrent neural networks, autoencoders, or generative adversarial networks). To address the various challenges of automatically processing and interpreting ultrasound images, deep learning techniques have been gradually applied to various types of ultrasound data (such as B-mode ultrasound, Doppler ultrasound, or contrast-enhanced ultrasound), acquired with a range of different probes, with the aim of improving image quality, for organ segmentation, device localization and tracking, for tissue characterization, and ultimately to improve disease diagnosis and therapeutic outcome. The papers in this special section seek to present and highlight the latest development on applying advanced deep learning techniques in ultrasound imaging.