UltraSeP: Sequence-aware Pre-training for Echocardiography Probe Movement Guidance

TL;DR

This paper introduces UltraSeP, a sequence-aware pre-training method for guiding echocardiography probe movement, which improves personalized cardiac structure understanding and reduces guidance errors in ultrasound imaging.

Contribution

The paper presents a novel sequence-aware self-supervised pre-training approach that captures personalized cardiac features for improved probe guidance in echocardiography.

Findings

Reduces probe guidance errors compared to baseline methods

Effectively learns personalized cardiac structures from large-scale data

Improves quality of standard plane image acquisition

Abstract

Echocardiography is an essential medical technique for diagnosing cardiovascular diseases, but its high operational complexity has led to a shortage of trained professionals. To address this issue, we introduce a novel probe movement guidance algorithm that has the potential to be applied in guiding robotic systems or novices with probe pose adjustment for high-quality standard plane image acquisition.Cardiac ultrasound faces two major challenges: (1) the inherently complex structure of the heart, and (2) significant individual variations. Previous works have only learned the population-averaged structure of the heart rather than personalized cardiac structures, leading to a performance bottleneck. Clinically, we observe that sonographers dynamically adjust their interpretation of a patient's cardiac anatomy based on prior scanning sequences, consequently refining their scanning strategies. Inspired by this, we propose a novel sequence-aware self-supervised pre-training method. Specifically, our approach learns personalized three-dimensional cardiac structural features by predicting the masked-out image features and probe movement actions in a scanning sequence. We hypothesize that if the model can predict the missing content it has acquired a good understanding of personalized cardiac structure. Extensive experiments on a large-scale expert scanning dataset with 1.67 million samples demonstrate that our proposed sequence-aware paradigm can effectively reduce probe guidance errors compared to other advanced baseline methods.