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arXiv · 2111.02461

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning

Baichuan Jiang·Alvin Chen·Shyam Bharat·Mingxin Zheng
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Abstract

Accurate, real-time segmentation of vessel structures in ultrasound image sequences can aid in the measurement of lumen diameters and assessment of vascular diseases. This, however, remains a challenging task, particularly for extremely small vessels that are difficult to visualize. We propose to leverage the rich spatiotemporal context available in ultrasound to improve segmentation of small-scale lower-extremity arterial vasculature. We describe efficient deep learning methods that incorporate temporal, spatial, and feature-aware contextual embeddings at multiple resolution scales while jointly utilizing information from B-mode and Color Doppler signals. Evaluating on femoral and tibial artery scans performed on healthy subjects by an expert ultrasonographer, and comparing to consensus expert ground-truth annotations of inner lumen boundaries, we demonstrate real-time segmentation using the context-aware models and show that they significantly outperform comparable baseline approaches.

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Baichuan Jiang, Alvin Chen, Shyam Bharat, Mingxin Zheng. 2021-11-03. Automatic ultrasound vessel segmentation with deep spatiotemporal context learning. https://arxiv.org/abs/2111.02461

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