Prompt-driven Universal Model for View-Agnostic Echocardiography
  Analysis
Sekeun Kim Hui Ren Peng Guo Abder-Rahman Ali Patrick Zhang Kyungsang Kim Xiang Li Quanzheng Li
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.