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.

翻译：摘要：分割一切模型（Segment Anything Model, SAM）作为杰出的通用图像分割模型，近期在医学图像分割领域引起了广泛关注。尽管SAM在自然图像上表现卓越，但面对医学图像，尤其是涉及低对比度、模糊边界、复杂形状及微小尺寸目标的图像时，其性能显著下降且泛化能力受限。本文提出SAMUS，一种专为超声图像分割设计的通用模型。与先前基于SAM的通用模型不同，SAMUS不仅追求更强的泛化性，还致力于降低部署成本，因此更适用于临床应用。具体而言，基于SAM，我们引入并行CNN分支，通过跨分支注意力机制将局部特征注入ViT编码器，以提升医学图像分割效果。随后，开发位置适配器与特征适配器，将SAM从自然领域适配至医学领域，并将输入尺寸从大尺寸（1024×1024）压缩至小尺寸（256×256），以实现更临床友好的部署。为验证性能，我们收集了包含约3万张图像、6.9万个掩膜及覆盖六类目标的综合超声数据集。大量对比实验表明，SAMUS在任务特定评估与泛化评估中均优于当前最先进的任务专用模型与通用基础模型。此外，SAMUS可部署于入门级GPU，因其已摆脱长序列编码的限制。代码、数据及模型将发布至https://github.com/xianlin7/SAMUS。