Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundation models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale ulti-organ, ulti-center, and ulti-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices.

中文翻译：

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USFM：通用超声基础模型，可推广到任务和器官，实现标签高效图像分析


不同器官和任务的通用性不足限制了超声（US）图像分析方法在智能医疗中的应用。建立一个通用的美国基金会模型有可能解决这些问题。然而，此类基础模型的发展在美国分析中遇到了固有的挑战，即数据库不足、质量低下、特征无效。在本文中，我们提出了一种通用的 US 基础模型，名为 USFM，可推广到不同的任务和器官，以实现标签高效的 US 图像分析。首先，建立了大规模的多器官、多中心、多设备的超声数据库，全面包含超过200万张超声图像。采用器官平衡抽样来进行公正的学习。然后，USFM 在足够的 US 数据库上进行自我监督预训练。为了从低质量US图像中提取有效特征，我们提出了一种空频双掩模图像建模方法。设计了一种高效的空间噪声添加恢复方法来稳健地学习有意义的美国信息，同时还采用了一种新颖的频带阻掩蔽学习方法来提取复杂的隐式灰度分布和纹理变化。对不同器官和疾病的分割、分类和图像增强的各种任务进行了大量的实验。与美国代表性图像分析模型的比较说明了USFM的普遍性和有效性。标签效率实验表明USFM仅用20%的注释就获得了稳健的性能，为US模型在临床实践中的快速发展奠定了基础。