Echocardiography segmentation for cardiac analysis is time-consuming and resource-intensive due to the variability in image quality and the necessity to process scans from various standard views. While current automated segmentation methods in echocardiography show promising performance, they are trained on specific scan views to analyze corresponding data. However, this solution has a limitation as the number of required models increases with the number of standard views. To address this, in this paper, we present a prompt-driven universal method for view-agnostic echocardiography analysis. Considering the domain shift between standard views, we first introduce a method called prompt matching, aimed at learning prompts specific to different views by matching prompts and querying input embeddings using a pre-trained vision model. Then, we utilized a pre-trained medical language model to align textual information with pixel data for accurate segmentation. Extensive experiments on three standard views showed that our approach significantly outperforms the state-of-the-art universal methods and achieves comparable or even better performances over the segmentation model trained and tested on same views.

本文提出了一种视图无关的超声心动图分析方法，通过 prompt 匹配和预训练的视觉模型学习不同视图的专用提示，并利用预训练的医学语言模型将文本信息与像素数据对齐，以实现精准的分割，大量实验证明该方法在三个标准视图上明显优于最新的通用方法，并实现与同视图训练和测试的分割模型相当甚至更好的性能。

面向视角无关心脏超声分析的启发式通用模型