Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis
This addresses the need for efficient cardiac analysis in medical imaging by reducing the number of models required for different echocardiography views, though it is incremental as it builds on existing prompt and pre-trained model techniques.
The paper tackles the problem of echocardiography segmentation across multiple standard views by introducing a prompt-driven universal model that uses prompt matching and a pre-trained medical language model to align textual and pixel data, achieving state-of-the-art performance and comparable results to view-specific models.
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.