Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images.

[1]  Pablo J. Blanco,et al.  Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets , 2020, European heart journal. Digital health.

[2]  P. Serruys,et al.  Evaluation of the Efficacy of Computed Tomographic Coronary Angiography in Assessing Coronary Artery Morphology and Physiology: Rationale and Study Design , 2020, Cardiology.

[3]  P. Huang,et al.  Comparison of Diagnostic Performance of Intracoronary Optical Coherence Tomography-based and Angiography-based Fractional Flow Reserve for Evaluation of Coronary Stenosis. , 2020, EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology.

[4]  G. Mintz,et al.  Effect of Intravascular Ultrasound-Guided Drug-Eluting Stent Implantation: 5-Year Follow-Up of the IVUS-XPL Randomized Trial. , 2020, JACC. Cardiovascular interventions.

[5]  J. Ge,et al.  TCT-352 Deep Learning–Based Fully Automated Segmentation of IVUS for Quantitative Measurement , 2019, Journal of the American College of Cardiology.

[6]  Mariana del Fresno,et al.  Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures , 2019, Comput. Methods Programs Biomed..

[7]  Anup Basu,et al.  Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net. , 2019, Ultrasonics.

[8]  Santanu Chaudhury,et al.  Ultrasound Image Segmentation: A Deeply Supervised Network With Attention to Boundaries , 2019, IEEE Transactions on Biomedical Engineering.

[9]  Yuanyuan Wang,et al.  IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution , 2019, Comput. Biol. Medicine.

[10]  Yeonggul Jang,et al.  Fully Automatic Segmentation of Coronary Arteries Based on Deep Neural Network in Intravascular Ultrasound Images , 2018, CVII-STENT/LABELS@MICCAI.

[11]  D. Molony,et al.  TCT-2 Deep IVUS: A machine learning framework for fully automatic IVUS segmentation , 2018, Journal of the American College of Cardiology.

[12]  Ji Yang,et al.  IVUS-Net: An Intravascular Ultrasound Segmentation Network , 2018, ICSM.

[13]  Xiaoguang Niu,et al.  Image Segmentation with Pyramid Dilated Convolution Based on ResNet and U-Net , 2017, ICONIP.

[14]  Pavel V Hushcha,et al.  Machine Learning Approaches in Cardiovascular Imaging , 2017, Circulation. Cardiovascular imaging.

[15]  G. Mintz,et al.  Intravascular imaging in coronary artery disease , 2017, The Lancet.

[16]  Zhenghui Hu,et al.  An artificial neural network method for lumen and media-adventitia border detection in IVUS , 2017, Comput. Medical Imaging Graph..

[17]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  G. Mintz,et al.  Effects of Intravascular Ultrasound-Guided Versus Angiography-Guided New-Generation Drug-Eluting Stent Implantation: Meta-Analysis With Individual Patient-Level Data From 2,345 Randomized Patients. , 2016, JACC. Cardiovascular interventions.

[19]  Gözde B. Ünal,et al.  Computerized Medical Imaging and Graphics Standardized evaluation methodology and reference database for evaluating IVUS image segmentation , 2013 .

[20]  E. Gerardo Mendizabal-Ruiz,et al.  Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach , 2013, Medical Image Anal..

[21]  P. Serruys,et al.  Comparison of intravascular ultrasound versus angiography-guided drug-eluting stent implantation: a meta-analysis of one randomised trial and ten observational studies involving 19,619 patients. , 2012, EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology.

[22]  Nassir Navab,et al.  A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images , 2012, IEEE Transactions on Information Technology in Biomedicine.

[23]  Elisa E. Konofagou,et al.  Automatic detection of blood versus non-blood regions on intravascular ultrasound (IVUS) images using wavelet packet signatures , 2008, SPIE Medical Imaging.

[24]  Marco Valgimigli,et al.  In vivo intravascular ultrasound-derived thin-cap fibroatheroma detection using ultrasound radiofrequency data analysis. , 2005, Journal of the American College of Cardiology.

[25]  M R Rees,et al.  In vivo validation of a novel semi-automated method for border detection in intravascular ultrasound images. , 2005, The British journal of radiology.

[26]  M. Girard Conflicts of interests , 2004 .

[27]  Johan H. C. Reiber,et al.  Automatic border detection in IntraVascular UltraSound images for quantitative measurements of the vessel, lumen and stent parameters , 2001, CARS.

[28]  C. Tracy,et al.  American College of Cardiology Clinical Expert Consensus Document on Standards for Acquisition, Measurement and Reporting of Intravascular Ultrasound Studies (IVUS). A report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents. , 2001, Journal of the American College of Cardiology.

[29]  G Kovalski,et al.  Three-dimensional automatic quantitative analysis of intravascular ultrasound images. , 2000, Ultrasound in medicine & biology.

[30]  Yongmin Kim,et al.  A methodology for evaluation of boundary detection algorithms on medical images , 1997, IEEE Transactions on Medical Imaging.

[31]  J F Cornhill,et al.  Automated morphometry of coronary arteries with digital image analysis of intravascular ultrasound. , 1997, American heart journal.

[32]  M T Mallus,et al.  Computerized assessment of coronary lumen and atherosclerotic plaque dimensions in three-dimensional intravascular ultrasound correlated with histomorphometry. , 1996, The American journal of cardiology.

[33]  C J Slager,et al.  Morphometric analysis in three-dimensional intracoronary ultrasound: an in vitro and in vivo study performed with a novel system for the contour detection of lumen and plaque. , 1996, American heart journal.

[34]  Weiqi Wang,et al.  Automatic segmentation of calcifications in intravascular ultrasound images using snakes and the contourlet transform. , 2010, Ultrasound in medicine & biology.

[35]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[36]  N. Bruining,et al.  A novel retrospective gating method for intracoronary ultrasound images based on image properties , 2003, Computers in Cardiology, 2003.