SAMUS: Adapting Segment Anything Model for Clinically-Friendly and
  Generalizable Ultrasound Image Segmentation
Xian Lin Yangyang Xiang Li Zhang Xin Yang Zengqiang Yan Li Yu
Segment anything model (SAM), an eminent universal image segmentation model,
has recently gathered considerable attention within the domain of medical image
segmentation. Despite the remarkable performance of SAM on natural images, it
grapples with significant performance degradation and limited generalization
when confronted with medical images, particularly with those involving objects
of low contrast, faint boundaries, intricate shapes, and diminutive sizes. In
this paper, we propose SAMUS, a universal model tailored for ultrasound image
segmentation. In contrast to previous SAM-based universal models, SAMUS pursues
not only better generalization but also lower deployment cost, rendering it
more suitable for clinical applications. Specifically, based on SAM, a parallel
CNN branch is introduced to inject local features into the ViT encoder through
cross-branch attention for better medical image segmentation. Then, a position
adapter and a feature adapter are developed to adapt SAM from natural to
medical domains and from requiring large-size inputs (1024x1024) to small-size
inputs (256x256) for more clinical-friendly deployment. A comprehensive
ultrasound dataset, comprising about 30k images and 69k masks and covering six
object categories, is collected for verification. Extensive comparison
experiments demonstrate SAMUS's superiority against the state-of-the-art
task-specific models and universal foundation models under both task-specific
evaluation and generalization evaluation. Moreover, SAMUS is deployable on
entry-level GPUs, as it has been liberated from the constraints of long
sequence encoding. The code, data, and models will be released at
https://github.com/xianlin7/SAMUS.