# Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis

arXiv: https://arxiv.org/abs/2404.05916 · HTML: https://mlsanity.com/paper/2404.05916/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023)
- [DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets](https://mlsanity.com/paper/2011.10217/) (2020)
- [U-Net Transformer: Self and Cross Attention for Medical Image Segmentation](https://mlsanity.com/paper/2103.06104/) (2021)
- [Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study](https://mlsanity.com/paper/2209.15517/) (2022)
- [UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner](https://mlsanity.com/paper/2304.03493/) (2023)
- [Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization](https://mlsanity.com/paper/2101.11075/) (2021)
- [MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography](https://mlsanity.com/paper/2309.13539/) (2023)
- [Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features](https://mlsanity.com/paper/2308.16483/) (2023)

## Abstract (arXiv)

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.