A dual-modal dynamic contour-based method for cervical vascular ultrasound image instance segmentation

We intend to develop a dual-modal dynamic contour-based instance segmentation method that is based on carotid artery and jugular vein ultrasound and its optical flow image, then we evaluate its performance in comparison with the classic single-modal deep learning networks.

We collected 2432 carotid artery and jugular vein ultrasound images and divided them into training, validation and test dataset by the ratio of 8:1:1. We then used these ultrasound images to generate optical flow images with clearly defined contours. We also proposed a dual-stream information fusion module to fuse complementary features between different levels extracted from ultrasound and optical flow images. In addition, we proposed a learnable contour initialization method that eliminated the need for manual design of the initial contour, facilitating the rapid regression of nodes on the contour to the ground truth points.

We verified our method by using a self-built dataset of carotid artery and jugular vein ultrasound images. The quantitative metrics demonstrated a bounding box detection mean average precision of 0.814 and a mask segmentation mean average precision of 0.842. Qualitative analysis of our results showed that our method achieved smoother segmentation boundaries for blood vessels.

The dual-modal network we proposed effectively utilizes the complementary features of ultrasound and optical flow images. Compared to traditional single-modal instance segmentation methods, our approach more accurately segments the carotid artery and jugular vein in ultrasound images, demonstrating its potential for reliable and precise medical image analysis.