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.

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客座社论：超声成像中的深度学习

在不同的成像方式中，超声波是用于可视化人体组织的最广泛的方式，因为它是低成本，非电离的，实时的，可实时反馈给超声检查者，操作方便，广泛可用且功能完善的超声波。在单个设置中生成的图像数量非常大。另一方面，超声成像具有依赖于用户和质量可变的缺点，这使得超声图像的自动解释通常非常困难。近年来，由于深度学习方法（包括卷积神经网络，递归神经网络，自动编码器或生成对抗网络）的出现，医学成像算法得到了显着改善。为了解决自动处理和解释超声图像的各种挑战，深度学习技术已逐渐应用于各种类型的超声数据（例如B模式超声，多普勒超声或对比增强超声），并通过一系列不同的方式获得旨在改善图像质量的探头，用于器官分割，设备定位和跟踪，组织表征，并最终改善疾病的诊断和治疗效果。本节的论文旨在介绍和强调在超声成像中应用高级深度学习技术的最新进展。使用一系列不同的探头获得的，目的是提高图像质量，进行器官分割，设备定位和跟踪，进行组织表征，并最终改善疾病的诊断和治疗效果。本节的论文旨在介绍和强调在超声成像中应用高级深度学习技术的最新进展。使用一系列不同的探头获得的，目的是提高图像质量，进行器官分割，设备定位和跟踪，进行组织表征，并最终改善疾病的诊断和治疗效果。本节的论文旨在介绍并强调在超声成像中应用高级深度学习技术的最新进展。