  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 foundational 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 Multi-organ, Multi-center,
and Multi-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.
