Automatic ultrasound vessel segmentation with deep spatiotemporal context learning
This addresses the challenge of visualizing and measuring extremely small vessels for vascular disease assessment, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of real-time segmentation of small vessels in ultrasound image sequences by leveraging spatiotemporal context, demonstrating that their context-aware models significantly outperform baseline approaches on femoral and tibial artery scans.
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.