Segmentation of arteriovenous malformations nidus and vessel in digital subtraction angiography images based on an iterative thresholding method

Lian, Yuxi; Wang, Yuanyuan; Yu, Jinhua; Guo, Yi; Chen, Liang

doi:10.1109/bmei.2015.7401483

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…However, this method is not applicable enough to segment the AVM nidus and vessel simultaneously. To handle this problem, Lian, et al [ 80 ] combined the global thresholding technique to distinguish the large nidus and local iterative thresholding technique to identify tiny vessel structures on DSA. For the detection and segmentation of AVMs after radiosurgery, such as gamma knife, fuzzy c-means clustering on T2-weighted MR images [ 81 , 82 ] and 3D V-net with deep supervision on CT [ 83 ] were also investigated.…”

Section: Resultsmentioning

confidence: 99%

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

Chen

et al. 2022

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Background: A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limited used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. Objective: This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. Methods: Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. Results: For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. For the algorithms, both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. Conclusion: Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

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Section: Resultsmentioning

confidence: 99%

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

Chen

et al. 2022

Self Cite

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“…The binarization based on block processing with different block processing strategies will produce different results with varying performances. We visually compare the noise and the missing of blood vessel wall generated by these strategies based on large amount of images, which is a widely used method for evaluating the performance of binarization methods [35][36][37]. After extensive testing, we find that the strategy of block processing into 40 subimages (5 L Â 8 W) is optimal.…”

Section: Preprocessingmentioning

confidence: 99%

Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography

Zhu

Ding

Tao

et al. 2021

Journal of Biophotonics

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We present an automatic lumen segmentation method using uniqueness of connected region for intravascular optical coherence tomography (IVOCT), which can effectively remove the effect on lumen segmentation caused by blood artifacts.Utilizing the uniqueness of vascular wall on A-lines, we detect the A-lines shared by multiple connected regions, identify connected regions generated by blood artifacts using traversal comparison of connected regions' location, shared ratio and area ratio and then remove all artifacts. We compare these three methods by 216 challenging images with severe blood artifacts selected from clinical 1076 IVOCT images. The metrics of the proposed method are evaluated including Dice index, Jaccard index and accuracy of 94.57%, 90.12%, 98.02%. Compared with automatic lumen segmentation based on the previous morphological feature method and widely used dynamic programming method, the metrics of the proposed method are significantly enhanced, especially in challenging images with severe blood artifacts.

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“…In order to solve the possible bifurcation problem and the non-single-pixel problem of the centerline, we improve the Zhang-Suen thinning algorithm. After analyzing the principle of the Zhang thinning algorithm and the thinned image, it is found that the cause of the non-single pixels in the thinned texture is that some of the deleted points are omitted because they do not meet the deletion conditions (6). We divide these points into three categories.…”

Section: Obtaining the Centerline Of Blood Vesselsmentioning

confidence: 99%

“…Compared with the original Zhang thinning algorithm, we use condition (12) instead of condition (6) to judge whether the target point needs to be deleted.…”

Section: Obtaining the Centerline Of Blood Vesselsmentioning

confidence: 99%

“…Traditional image processing methods can be divided into several different methods: thresholding method, region growing method, statistical region merging method, and matched filter. Lian et al [6] used an iterative threshold-based method to extract deformed parts of blood vessels. They first segmented the image into several sub-images and divided these sub-images into two different categories using the maximum inter-class variance method.…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Segmentation of the Coronary Arteries of Interest and Diameter Measurement

Jiang

Pan

et al. 2020

IEEE Access

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Cardiovascular disease is a severe threat to human health. The assessment of the degree of cardiovascular stenosis is very important for the diagnosis of cardiovascular disease. The segmentation of coronary angiographic images is the basis of analyzing the degree of stenosis. In this paper, a method that evaluates explicitly the degree of stenosis of blood vessels of interest is proposed. This method first extracts the vessels of interest by interactive segmentation, then thins it, and then calculates the diameter by edge intersection method. The average Dice Similarity Coefficient (DSC) value of the proposed segmentation method exceeds 92%, and the average Jaccard Similarity (JAC) value is over 86%. The area under the ROC curve value of the method is larger than the u-net method and the multiscale Hessian method. The results indicate that the interactive method can have good segmentation results and meet the general segmentation requirements. The qualitative and quantitative evaluation of the diameter measurement effect also shows that the effect of the corresponding diameter measurement method can reflect the change of the thickness of the blood vessel and basically meet the needs of clinical use.

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Segmentation of arteriovenous malformations nidus and vessel in digital subtraction angiography images based on an iterative thresholding method

Cited by 3 publications

References 14 publications

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography

A New Method for Segmentation of the Coronary Arteries of Interest and Diameter Measurement

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