Boundary segmentation based on modified random walks for vascular Doppler optical coherence tomography images

Huang, Yong; Wu, Chuanchao; Xia, Shiyu; Liu, Lu; Chen, Shanlin; Tong, Dedi; Ai, Danni; Yang, Jian; Wang, Yongtian

doi:10.3788/col201917.051001

Cited by 3 publications

(4 citation statements)

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“…It clearly shows that the lumen area becomes narrower at the thrombosis position, which points out by black circle. The average radius of one cross-sectional image in 10 angular directions with their stand deviations is shown in Figure 6(b), which is consistent with the segmentation results in [21,46]. The average radius of the vessel gets smaller at the thrombus point, which is easier to result in turbulence and contribute to the thrombosis formation.…”

Section: Discussionsupporting

confidence: 82%

“…The average radius of the vessel gets smaller at the thrombus point, which is easier to result in turbulence and contribute to the thrombosis formation. Table 2 shows the performance of the proposed method compared to the traditional random walk method [21] and CU-Net method [46] in terms of the dice coefficient and consuming time. These three methods have high segmentation accuracy for Doppler OCT intensity map and phase map.…”

Section: Discussionmentioning

confidence: 99%

“…And then used global gray scale statistics to optimize and achieve image segmentation. Huang et al [21] used the Laplacian operator to perform iterative calculation by changing the calculation operator of the random walk, which showed good robustness to the weak boundary of OCT images. The graphbased approach is used to achieve segmentation of retinal in time domain OCT images [22][23][24] and OCT coronary vessel images [25].…”

Section: Introductionmentioning

confidence: 99%

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Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning

Wu,

Wang,

Xue

et al. 2024

Preprint

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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.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning

Wu,

Wang,

Xue

et al. 2024

Preprint

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“…In particular, strain information along the rock bolts should be collected to reveal long‐term movements of the rock mass and to allow an operator to take necessary precautions . With the development of optical fiber technology and its use in sensing and measurement, this technology is increasingly used in monitoring stress, overburden deformation, temperature, humidity, and moisture . Lately, optical fibers have been used in monitoring the stress of bolts .…”

Section: Introductionmentioning

confidence: 99%

Monitoring and correction of the stress in an anchor bolt based on Pulse Pre‐Pumped Brillouin Optical Time Domain Analysis

Liu

Chai

Chen

et al. 2020

Energy Science & Engineering

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Bolt is an important connection in underground construction and monitoring its stress characteristics is essential to evaluate the effectiveness of bolt. The strain information collected along a rock bolt reveals the long-term movement characteristics of rock mass, which greatly helps toward disaster prevention and risk warning.Grooving on the surface of a bolt and embedding an optical fiber is one of the main methods for monitoring the anchorage performance and stress distribution around the bolt in underground engineering. In this study, Pulse Pre-Pumped Brillouin Optical Time Domain Analysis (PPP-BOTDA) technology is used to evaluate the stress, axial force, and shear stress distribution of the bolt under tensile load for different types (grooved and nongrooved) of bolts embedded in concrete with resin as anchoring agent. The results show that the PPP-BOTDA sensor can be used to monitor the stress state of the bolt embedded in concrete with resin as anchoring agent when the bolt is subjected to a tensile force. However, the apparent stress value measured in this way is not the actual stress value of the bolt, but is an increased value caused due to the groove. The closer to the exposed end of the bolt, the greater is the deviation between the apparent stress value and the actual stress value of the bolt. Based on the monitoring results from this experimental study, a stress reduction calculation method is proposed, which can change the measured value into the actual stress of the bolt. K E Y W O R D Saxial force, bolt, PPP-BOTDA technology, pullout test, shear stress 2012 |

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Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning

Wu,

Wang,

Xue

et al. 2024

Electronics

View full text Add to dashboard Cite

Microscopic and ultramicroscopic vascular sutures are indispensable in surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, which may feature vascular patency, narrowness, and blockage, determines the success rate of the operation. If we can take advantage of the golden window of opportunity after blood vessel suture and before muscle tissue suture to achieve an accurate and objective assessment of blood vessel status, this will not only reduce medical costs but will also offer social benefits. Doppler optical coherence tomography enables the high-speed, high-resolution imaging of biological tissues, especially microscopic and ultramicroscopic blood vessels. By using Doppler optical coherence tomography to image the sutured blood vessels, a three-dimensional structure of the blood vessels and blood flow information can be obtained. By extracting the contour of the blood vessel wall and the contour of the blood flow area, the three-dimensional shape of the blood vessel can be reconstructed in three dimensions, providing 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 automatically and simultaneously 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 to the traditional random walk segmentation algorithm and cascade neural network method, the proposed model can produce the vessel boundary from the intensity image and the lumen area boundary from the corresponding phase image simultaneously, achieving an average testing segmentation accuracy of 0.967 and taking, on average, 0.63 s. This method can realize system integration more easily and has great potential for clinical evaluations. It is expected to be applied to the evaluation of microscopic and ultramicroscopic vascular status in microvascular anastomosis.

show abstract

Boundary segmentation based on modified random walks for vascular Doppler optical coherence tomography images

Cited by 3 publications

References 0 publications

Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning

Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning

Monitoring and correction of the stress in an anchor bolt based on Pulse Pre‐Pumped Brillouin Optical Time Domain Analysis

Boundary Segmentation of Vascular Images in Fourier Domain Doppler Optical Coherence Tomography Based on Deep Learning

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