Protect the Brain When Treating the Heart: A Convolutional Neural Network for Detecting Emboli

arXiv cs.AI / 4/27/2026

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Key Points

The paper addresses the challenge of detecting gaseous microemboli (GME) during cardiac structural interventions using transthoracic ultrasound.
It proposes a 2.5D U-Net–based convolutional neural network that segments GME in space-time connected ultrasound data to improve detection reliability.
The method is designed to be robust to operator-dependent imaging views, high-velocity signal characteristics, and background structures that look similar to the targets.
It reports high segmentation accuracy and real-time execution speed, enabling integration into patient-monitoring surgical protocols for tracking GME area over time.

Abstract

Gaseous microemboli (GME) represent a common complication of cardiac structural interventions across both surgical and transcatheter approaches. Transthoracic cardiac ultrasound imaging represents a convenient methodology to visualize the presence of circulating GME. However, their detection and quantification are far from trivial due to operator-dependent view, high velocity, and objects with similar structure in the background. Here, we propose an approach based on a 2.5D U-Net architecture to segment GME in space-time connected data. Such an approach yields robust detection against the background and high segmentation accuracy while retaining real-time execution speed. These properties facilitated the integration of the proposed pipeline into patient-monitoring surgical protocols, providing the quantification of GME area over time.