用于超声图像解释和扫描引导的语义场景图
Semantic Scene Graph for Ultrasound Image Explanation and Scanning Guidance
AI总结：
本研究为超声图像引入语义场景图（SG），采用基于transformer的单阶段方法计算SG并结合LLMs生成易于理解的图像解释，同时利用预测的SG引导非专业用户发现缺失解剖结构，实现更标准化的超声扫描，验证了该方法在提升超声可解释性和可用性方面的潜力。
摘要：
由于成像和采集参数差异导致的显著视觉变异性，理解医学超声成像一直是一个长期存在的挑战。大型语言模型（LLMs）的最新进展已被用于自动生成面向具备充足生理学知识的临床医生的术语丰富的摘要。然而，非专业用户（例如在即时超声设置中）对改善超声可解释性和基本扫描引导日益增长的需求尚未得到探索。在本研究中，我们首先为超声图像引入场景图（SG），以向普通人解释图像内容并为超声扫描提供引导。超声SG首先使用基于transformer的单阶段方法进行计算，消除了对显式目标检测的需求。为了为普通人生成易于理解的图像解释，随后使用用户查询通过LLMs进一步细化抽象的SG表示。此外，我们探索了预测的SG在引导超声扫描以发现当前成像视图中缺失解剖结构方面的潜力，协助普通用户实现更标准化和完整的解剖探索。这种基于SG的图像解释和扫描引导的有效性已在五名志愿者的左右颈部区域（包括颈动脉和甲状腺）的图像上得到验证。结果证明了该方法通过增强超声对普通人的可解释性和可用性，从而最大化普及超声的潜力。
英文摘要：
Understanding medical ultrasound imaging remains a long-standing challenge due to significant visual variability caused by differences in imaging and acquisition parameters. Recent advancements in large language models (LLMs) have been used to automatically generate terminology-rich summaries orientated to clinicians with sufficient physiological knowledge. Nevertheless, the increasing demand for improved ultrasound interpretability and basic scanning guidance among non-expert users, e.g., in point-of-care settings, has not yet been explored. In this study, we first introduce the scene graph (SG) for ultrasound images to explain image content to ordinary and provide guidance for ultrasound scanning. The ultrasound SG is first computed using a transformer-based one-stage method, eliminating the need for explicit object detection. To generate a graspable image explanation for ordinary, the user query is then used to further refine the abstract SG representation through LLMs. Additionally, the predicted SG is explored for its potential in guiding ultrasound scanning toward missing anatomies within the current imaging view, assisting ordinary users in achieving more standardized and complete anatomical exploration. The effectiveness of this SG-based image explanation and scanning guidance has been validated on images from the left and right neck regions, including the carotid and thyroid, across five volunteers. The results demonstrate the potential of the method to maximally democratize ultrasound by enhancing its interpretability and usability for ordinaries.
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