Wu, C.; Wang, Z.; Xue, P.; Liu, W. Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning. Electronics2024, 13, 2516.
Wu, C.; Wang, Z.; Xue, P.; Liu, W. Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning. Electronics 2024, 13, 2516.
Wu, C.; Wang, Z.; Xue, P.; Liu, W. Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning. Electronics2024, 13, 2516.
Wu, C.; Wang, Z.; Xue, P.; Liu, W. Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning. Electronics 2024, 13, 2516.
Abstract
Microscopic and ultra-microscopic vascular sutures are indispensable in the surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, such as vascular patency, narrow, and blocked, determines the success rate of the operation. If we can grasp the golden window period after blood vessel suture and before muscle tissue suture to achieve accurate and objective assessment of blood vessel status, it can not only reduce medical costs but also improve social benefits. Doppler optical coherence tomography enables high-speed, high-resolution imaging of biological tissues, especially microscopic and ultra-microscopic blood vessels. Using Doppler optical coherence tomography to image the sutured blood vessels, not only the three-dimensional structure of the blood vessels, but also the blood flow information can be obtained. By extracting the contour of blood vessel wall and the contour of blood flow area, the three-dimensional shape of blood vessel can be reconstructed in three dimensions, which provides parameter support for the assessment of blood vessel status. In this work, we propose a neural network-based multi-classification deep learning model that can simultaneously and automatically extract blood vessel boundaries from Doppler OCT vessel intensity images and the contours of blood flow regions from corresponding Doppler OCT vessel phase images. Compared with the traditional random walk segmentation algorithm and cascade neural network method, the proposed model has better performance. This method is easier to realize system integration and has great potential for clinical evaluation. It is expected to be applied to the evaluation of microscopic and ultra-microscopic vascular status in microvascular anastomosis.
Keywords
Optical coherence tomography; Image segmentation; Deep learning; Microvascular anastomosis
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
