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

• Published in: Mathematical biosciences and engineering : MBE Vol. 21; no. 1; pp. 1038 - 1057
• Main Authors: Chang, Chenkai, Qi, Fei, Xu, Chang, Shen, Yiwei, Li, Qingwu
• Format: Journal Article
• Language: English
• Published: United States AIMS Press 01.01.2024
• Subjects: Carotid Arteries - diagnostic imaging; cervical vascular; deep learning; dual-modal; Image Processing, Computer-Assisted - methods; instance segmentation; Ultrasonography; ultrasound-optical flow image; deep learning; instance segmentation; dual-modal; cervical vascular; ultrasound-optical flow image
• ISSN: 1551-0018; 1551-0018
• DOI: 10.3934/mbe.2024043

Objectives: 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. Method: 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. Results: 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. Conclusions: 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.