Automatic Plaque Segmentation in Coronary Optical Coherence Tomography Images
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Hongrui Wang | Feng Lin | Yechen Han | Huaqi Zhang | Guanglei Wang | Yan Li | Feng Lin | Yechen Han | Huaqi Zhang | Guanglei Wang | Hongrui Wang | Yan Li
[1] Bulat Ibragimov,et al. Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks , 2017, Medical physics.
[2] Mark Hewko,et al. Detection of Atherosclerotic Plaque from Optical Coherence Tomography Images Using Texture-Based Segmentation , 2015 .
[3] Ming Zhang,et al. Multiresolution Bilateral Filtering for Image Denoising , 2008, IEEE Transactions on Image Processing.
[4] Jianbo Shi,et al. Convolutional Random Walk Networks for Semantic Image Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Martin Styner,et al. Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.
[6] Theo van Walsum,et al. Semi-automated Quantification of Fibrous Cap Thickness in Intracoronary Optical Coherence Tomography , 2014, IPCAI.
[7] Gareth Funka-Lea,et al. Multi-label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials , 2004, ECCV Workshops CVAMIA and MMBIA.
[8] Yusuke Fujino,et al. Optical coherence tomography versus intravascular ultrasound to evaluate coronary artery disease and percutaneous coronary intervention. , 2013, JACC. Cardiovascular interventions.
[9] Salim Yusuf,et al. Effective approaches to address the global cardiovascular disease burden , 2017, Current opinion in cardiology.
[10] M. Fornage,et al. Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association , 2017, Circulation.
[11] 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..
[12] Atsushi Tanaka,et al. Fibroatheroma identification in Intravascular Optical Coherence Tomography images using deep features , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[13] Xianghua Xie,et al. Computer Vision Techniques for Transcatheter Intervention , 2015, IEEE Journal of Translational Engineering in Health and Medicine.
[14] Dimitrios I Fotiadis,et al. Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images , 2014, Journal of biomedical optics.
[15] T. Kume,et al. Current clinical applications of coronary optical coherence tomography , 2017, Cardiovascular Intervention and Therapeutics.
[16] Boudewijn P. F. Lelieveldt,et al. Automatic plaque characterization and vessel wall segmentation in magnetic resonance images of atherosclerotic carotid arteries , 2004, SPIE Medical Imaging.
[17] Ali Selamat,et al. Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images , 2017, Appl. Soft Comput..
[18] Mamoru Hoshi,et al. Fast Computation of Similarity Based on Jaccard Coefficient for Composition-Based Image Retrieval , 2009, PCM.
[19] Song-Hai Zhang,et al. Multi-Task Learning for Food Identification and Analysis with Deep Convolutional Neural Networks , 2016, Journal of Computer Science and Technology.
[20] Shouliang Qi,et al. CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images , 2018, Biomedical engineering online.
[21] Marco Costa,et al. Coronary optical coherence tomography: A practical overview of current clinical applications. , 2016, Revista portuguesa de cardiologia : orgao oficial da Sociedade Portuguesa de Cardiologia = Portuguese journal of cardiology : an official journal of the Portuguese Society of Cardiology.
[22] Dimos Baltas,et al. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk , 2017, Medical physics.
[23] Xiaoya Guo,et al. A Machine Learning-Based Method for Intracoronary OCT Segmentation and Vulnerable Coronary Plaque Cap Thickness Quantification , 2019, International Journal of Computational Methods.
[24] Ioanna Chouvarda,et al. Clinical validation of an algorithm for rapid and accurate automated segmentation of intracoronary optical coherence tomography images. , 2014, International journal of cardiology.
[25] Francesco Prati,et al. Optical coherence tomography features of angiographic complex and smooth lesions in acute coronary syndromes , 2015, The International Journal of Cardiovascular Imaging.
[26] Elsa D. Angelini,et al. Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules , 2017, MICCAI.