Stroke is one of the leading causes of death and disability around the world. Carotid artery stenosis （CAS） and cardiac lesions are important contributing factors to ischemic stroke， and ultrasound imaging has shown great potential in diagnosing ischemic stroke caused by CAS and cardiac lesions. But ultrasound images present significant segmentation challenges due to noise and blurred boundaries. To address this issue， the MSC-LSAM algorithm， a multi-scale crossed dual encoder network for ultrasound image segmentation is proposed. It aims to achieve rapid and accurate segmentation of carotid and cardiac cavities， assisting physicians in disease diagnosis. In the MSC-LSAM， the encoder part parallels a segment anything model （SAM） vision encoder and an UNet encoder， while the decoder part utilizes an UNet decoder. In the SAM image encoder， we froze the pretrained SAM image encoder and introduce efficient adapter blocks in Transformer layers， referred to as learnable SAM （LSAM）. LSAM maintains learning capability and high generalization ability while having a low number of parameters. In the global UNet network， we incorporate the multi-scale cross-axial attention （MCA） blocks to achieve cross-fusion of multi-scale features between different axes， effectively enhancing edge segmentation capabilities and suppressing model overfitting. Following the parallel encoders， the efficient channel attention （ECA） block is added to enable integration of multi-scale features from dual encoders， reducing incorrect segmentation caused by feature level mismatches. MSC-LSAM achieves good performance on both the publicly available cardiac ultrasound dataset of CAMUS and the self-constructed carotid artery ultrasound dataset of CAUS. Average dice similarity coefficients （DSCs） for the segmentation of the two-chamber （2CH） and four-chamber （4CH） datasets in CAMUS reach 0.927 and 0.934， respectively； while the average DSC for the CAUS dataset reaches 0.917. MSC-LSAM achieves good segmentation accuracy in tasks of carotid lumen and cardiac chamber ultrasound image segmentation， surpassing mainstream segmentation algorithms， and shows promising application prospects.