Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

Roy, Sanjiban Sekhar; Hsu, Ching‐Hsien; Samaran, Akash; Goyal, Ranjan; Pande, Arindam; Balas, Valentina E.

doi:10.32604/cmes.2023.019644

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…However, different evaluating metrics and vessel mask principles were used in these studies, making it difficult to compare the achieved results among varied models. Moreover, some of the research relies on small training data sets and does not provide performance results on external data sets 16 , 35 , 36 , raising concerns about the models' ability to generalize. Therefore, in this research, we re-trained all the comparison models with our CMUH study Dataset and validated them on an independent DCA1 Dataset.…”

Section: Discussionmentioning

confidence: 99%

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images

Chang,

Lin,

Wang

et al. 2024

Sci Rep

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Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.

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Section: Discussionmentioning

confidence: 99%

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images

Chang,

Lin,

Wang

et al. 2024

Sci Rep

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“…This is due to the way they are designed and constructed, intended as they are to maintain and interpret the spatial structure of input data, an attribute that is vital for the accurate assessment of medical images. For example, Roy et al [91] applied CNNs to cardiac image segmentation to diagnose coronary artery disease (CAD). CNNs were used to analyze 2D X-ray images, significantly enhancing image segmentation accuracy and setting new standards in medical image analysis.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Rudnicka,

Proniewska,

Perkins

et al. 2024

Electronics

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Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.

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“…Many years ago, cardiovascular disorders in North America were the primary cause of mortality [1]. Significant advances in cardiology patient diagnosis have been accomplished in the past ten years, increasing patients' probability of survival [2].…”

Section: Introductionmentioning

confidence: 99%

Efficient Hybrid Deep Learning Network Model for Segmentation and Classification of Heart Angiographic Images

Devi,

Ramaswami

2024

Nano Biomedicine and Engineering

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Heart angiography is a test that allows the treating physician to identify heart vessel abnormalities. The concerned doctor must spend a lot of time on this diagnosis. The heart vessels' targeted areas are segmented, and the blood vessel types are classified in the proposed approach. The heart angiography segmentation and classification provide essential information for the patient and the doctor. In contrast, the charge for cardiac angiography is a risky, mentally challenging task for the doctor (a heart specialist). Improved transparency and faster detection of heart diseases can result from automated segmentation and categorization of representations of heart blood vessels. Since accuracy is critical in classification, computer vision researchers are introducing several techniques; however, achieving high accuracy remains challenging when classifying heart diseases. This research proposes a multi-class ensemble classification mechanism-based computer-aided hybrid deep learning (DL) network model for localizing human blood vessels inside heart coronary computed tomography (CT) angiography images. First, the modified U-Net model is used to segment the heart blood vessels, and the self-attention network (SAT-Net) model is used to extract various features from the segmented blood vessel images. In segmented human heart blood vessels, low-level features are retrieved, including geometrical, statistical, and texture features. The enhanced elman spike neural network (EESNN) model has been used to finally classify cardiac blood vessels into four distinct groups: blood flow-reduced, narrow, block, and regular vessels. The suggested strategy has produced the best results, creating a conducive environment for cardiologists to diagnose heart-related disorders that is quick, simple, accurate, and time-saving.

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

Cited by 7 publications

References 32 publications

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images

Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review

Efficient Hybrid Deep Learning Network Model for Segmentation and Classification of Heart Angiographic Images

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