Automatic classification and segmentation of atherosclerotic plaques in the intravascular optical coherence tomography (IVOCT)
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Yang Chen | U. Sadat | Z. Teng | Wei Gao | Jinhua Shen | Yupeng Wang | Hui Tang | Xu Ji | Zhenquan Zhang | Y. He | Jin Zheng | Mingxin Wang
[1] Xiaoying Tang,et al. Current research and future prospects of IVOCT imaging‐based detection of the vascular lumen and vulnerable plaque , 2022, Journal of biophotonics.
[2] U. Raghavendra,et al. Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization , 2021, International journal of environmental research and public health.
[3] T. Akasaka,et al. Evaluation of coronary plaques and atherosclerosis using optical coherence tomography , 2021, Expert review of cardiovascular therapy.
[4] David L. Wilson,et al. Segmentation of Coronary Calcified Plaque in Intravascular OCT Images Using a Two-Step Deep Learning Approach , 2020, IEEE Access.
[5] H. Bezerra,et al. Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features , 2020, Scientific Reports.
[6] Sheng-Shou Hu,et al. China cardiovascular diseases report 2018: an updated summary , 2020, Journal of geriatric cardiology : JGC.
[7] F. Cheriet,et al. An automatic diagnostic system of coronary artery lesions in Kawasaki disease using intravascular optical coherence tomography imaging , 2019, Journal of biophotonics.
[8] Hongrui Wang,et al. Automatic Plaque Segmentation in Coronary Optical Coherence Tomography Images , 2019, Int. J. Pattern Recognit. Artif. Intell..
[9] David Wilson,et al. Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images , 2018, Journal of medical imaging.
[10] F. Cheriet,et al. Characterization of coronary artery pathological formations from OCT imaging using deep learning. , 2018, Biomedical optics express.
[11] Nils Gessert,et al. Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks , 2018, IEEE Transactions on Medical Imaging.
[12] Yih Miin Liew,et al. Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography , 2017, Journal of biomedical optics.
[13] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[14] M. Fornage,et al. Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association , 2017, Circulation.
[15] F. Cheriet,et al. Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. , 2017, Biomedical optics express.
[16] Martin Villiger,et al. Automatic classification of atherosclerotic plaques imaged with intravascular OCT. , 2016, Biomedical optics express.
[17] Charis Costopoulos,et al. Direct Comparison of Virtual-Histology Intravascular Ultrasound and Optical Coherence Tomography Imaging for Identification of Thin-Cap Fibroatheroma , 2015, Circulation. Cardiovascular imaging.
[18] Simona Celi,et al. In-vivo segmentation and quantification of coronary lesions by optical coherence tomography images for a lesion type definition and stenosis grading , 2014, Medical Image Anal..
[19] Dalin Tang,et al. Image-based modeling for better understanding and assessment of atherosclerotic plaque progression and vulnerability: data, modeling, validation, uncertainty and predictions. , 2014, Journal of biomechanics.
[20] E. Feldmann,et al. Management Strategies for Asymptomatic Carotid Stenosis , 2013, Annals of Internal Medicine.
[21] V. Fuster,et al. Histopathologic characteristics of atherosclerotic coronary disease and implications of the findings for the invasive and noninvasive detection of vulnerable plaques. , 2013, Journal of the American College of Cardiology.
[22] J. Spence. Asymptomatic carotid stenosis. , 2013, Circulation.
[23] Akiko Maehara,et al. Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the International Working Group for Intravascular Optical Coherence Tomography Standardization and Validation. , 2012, Journal of the American College of Cardiology.
[24] Andrew M. Rollins,et al. Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis , 2011, BiOS.
[25] Tony F. Chan,et al. Active contours without edges , 2001, IEEE Trans. Image Process..
[26] Tony F. Chan,et al. Active Contours without Edges for Vector-Valued Images , 2000, J. Vis. Commun. Image Represent..
[27] Tong Jia,et al. Optical Coherence Tomography Vulnerable Plaque Segmentation Based on Deep Residual U-Net. , 2019, Reviews in cardiovascular medicine.
[28] B E Bouma,et al. Imaging of coronary artery microstructure (in vitro) with optical coherence tomography. , 1996, The American journal of cardiology.