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: High-intensity focused ultrasound (HIFU) therapy represents a noninvasive surgical approach to treat uterine fibroids. The operation of HIFU therapy relies on the information provided by medical images. In current HIFU therapy, all operations such as positioning of the lesion in magnetic resonance (MR) and ultrasound (US) images are manually performed by specifically trained doctors. Manual processing is an important limitation of the efficiency of HIFU therapy. In this paper, we aim to provide an automatic and accurate image guidance system, intelligent diagnosis, and treatment strategy for HIFU therapy by combining multimodality information. : In intelligent HIFU therapy, medical information and treatment strategy are automatically processed and generated by a real-time image guidance system. The system comprises a novel multistage deep convolutional neural network for preoperative diagnosis and a nonrigid US lesion tracking procedure for HIFU intraoperative image-assisted treatment. In the process of intelligent therapy, the treatment area is determined from the autogenerated lesion area. Based on the autodetected treatment area, the HIFU foci are distributed automatically according to the treatment strategy. Moreover, an image-based unexpected movement warning and other physiological monitoring are used during the intelligent treatment procedure for safety assurance. : In the experiment, we integrated the intelligent treatment system on a commercial HIFU treatment device, and eight clinical experiments were performed. In the clinical validation, eight randomly selected clinical cases were used to verify the feasibility of the system. The results of the quantitative experiment indicated that our intelligent system met the HIFU clinical tracking accuracy and speed requirements. Moreover, the results of simulated repeated experiments confirmed that the autodistributed HIFU focus reached the level of intermediate clinical doctors. Operations performed by junior- or middle-level operators with the assistance of the proposed system can reach the level of operation performed by senior doctors. Various experiments prove that our proposed intelligent HIFU therapy process is feasible for treating common uterine fibroid cases. : We propose an intelligent HIFU therapy for uterine fibroid which integrates multiple medical information processing procedures. The experiment results demonstrated that the proposed procedures and methods can achieve monitored and automatic HIFU diagnosis and treatment. This research provides a possibility for intelligent and automatic noninvasive therapy for uterine fibroid.

高强度聚焦超声（HIFU）治疗代表了一种非侵入性的手术方法，可用于治疗子宫肌瘤。HIFU 治疗的操作依赖于医学图像提供的信息。在当前的 HIFU 治疗中，所有操作，如磁共振（MR）和超声（US）图像中病变的定位，都是由经过专门培训的医生手动完成的。手动处理是 HIFU 治疗效率的一个重要限制。在本文中，我们旨在通过结合多模态信息，为 HIFU 治疗提供一种自动、准确的图像引导系统、智能诊断和治疗策略。

在智能 HIFU 治疗中，医疗信息和治疗策略由实时图像引导系统自动处理和生成。该系统包括一个用于术前诊断的新型多级深度卷积神经网络和一个用于 HIFU 术中图像辅助治疗的非刚性 US 病变跟踪程序。在智能治疗过程中，从自动生成的病变区域确定治疗区域。基于自动检测到的治疗区域，根据治疗策略自动分布 HIFU 焦点。此外，在智能治疗过程中使用基于图像的意外运动警告和其他生理监测，以确保安全。

在实验中，我们将智能治疗系统集成到商用 HIFU 治疗设备上，并进行了八项临床实验。在临床验证中，我们使用了 8 个随机选择的临床病例来验证系统的可行性。定量实验的结果表明，我们的智能系统满足 HIFU 临床跟踪精度和速度要求。此外，模拟重复实验的结果证实，自动分布的 HIFU 焦点达到了中级临床医生的水平。在该系统的辅助下，由初级或中级操作人员进行的操作可以达到由高级医生进行的操作水平。各种实验证明，我们提出的智能 HIFU 治疗过程对于治疗常见的子宫肌瘤病例是可行的。

我们提出了一种用于治疗子宫肌瘤的智能 HIFU 治疗方法，该方法集成了多个医疗信息处理程序。实验结果表明，所提出的程序和方法可以实现监控和自动 HIFU 诊断和治疗。这项研究为子宫肌瘤的智能和自动无创治疗提供了一种可能性。