The performance of segment anything model (SAM) is satisfactory in natural images, but it exhibits obvious performance degradation and limited generalization ability in the context of high-intensity focused ultrasound(HIFU) treatment monitoring images. There are two main problems: (1) the dataset of HIFU treatment monitoring images is excessively rare due to data collection limitations and privacy protection. (2) the noise and artefacts of B-mode ultrasonic images lead to low contrast and blurred boundaries in the target region. In this work, we propose a combined Latent Diffusion of bounding SAM for HIFU target region segmentation, called LD-BSAM. We designed and incorporated a data filtering module in the Latent Diffusion model to generate high-quality HIFU ultrasound surveillance images to assist in BSAM training. At the same time, innovated feature extractor and bounding extractor are added to SAM to extract HIFU treatment of target region more accurately. The experimental results show that the ultrasound surveillance images generated by the Latent Diffusion model in this paper exhibit better metrics on FID and LPIPS than other generative models. Compared to other 19 state-of-the-art segmentation models, the model in this paper works best in the ultrasound surveillance dataset of uterine fibroids for HIFU clinical treatment. To further explore the generalisability of the proposed algorithm, validation was continued on the breast ultrasound public datasets (BUSI, BUSC, BUS) and the thyroid ultrasound public dataset TN3K. The code, data and models will be released at https://github.com/425877/LD-BSAM .