1. Articles from Juhwan Lee

    1-3 of 3
    1. Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

      Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

      For intravascular OCT (IVOCT) images, we developed an automated atherosclerotic plaque characterization method that used a hybrid learning approach, which combined deep-learning convolutional and hand-crafted, lumen morphological features. Processing was done on innate A-line units with labels fibrolipidic (fibrous tissue followed by lipidous tissue), fibrocalcific (fibrous tissue followed by calcification), or other. We trained/tested on an expansive data set (6,556 images), and performed an active learning, relabeling step to improve noisy ground truth labels. Conditional random field was an important post-processing step to reduce classification errors. Sensitivities/specificities were 84.8%/97.8% and 91.4%/95.7 ...

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    2. Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence Tomography

      Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence Tomography

      Intravascular optical coherence tomography (IVOCT) is used to assess stent tissue coverage and malapposition in stent evaluation trials. We developed the OCT Image Visualization and Analysis Toolkit for Stent (OCTivat-Stent), for highly automated analysis of IVOCT pullbacks. Algorithms automatically detected the guidewire, lumen boundary, and stent struts; determined the presence of tissue coverage for each strut; and estimated the stent contour for comparison of stent and lumen area. Strut-level tissue thickness, tissue coverage area, and malapposition area were automatically quantified. The software was used to analyze 292 stent pullbacks. The concordance-correlation-coefficients of automatically measured stent and lumen areas and independent ...

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    3. Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

      Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

      Accurate identification of coronary plaque is very important for cardiologists when treating patients with advanced atherosclerosis. We developed fully-automated semantic segmentation of plaque in intravascular OCT images. We trained/tested a deep learning model on a folded, large, manually annotated clinical dataset. The sensitivities/specificities were 87.4%/89.5% and 85.1%/94.2% for pixel-wise classification of lipidous and calcified plaque, respectively. Automated clinical lesion metrics, potentially useful for treatment planning and research, compared favorably (<4%) with those derived from ground-truth labels. When we converted the results to A-line classification, they were significantly better (p < 0.05) than ...

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    1-3 of 3
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  2. Topics in the News

    1. (3 articles) Case Western Reserve University
    2. (3 articles) David L. Wilson
    3. (3 articles) Hiram G. Bezerra
    4. (2 articles) Nanjing University of Science and Technology
    5. (1 articles) Massachusetts Institute of Technology
    6. (1 articles) Cleveland Clinic
    7. (1 articles) Hong Lu
    8. (1 articles) Zhao Wang
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    Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images Application and Evaluation of Highly Automated Software for Comprehensive Stent Analysis in Intravascular Optical Coherence Tomography Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features CT texture analysis of vulnerable plaques on optical coherence tomography Peri-stent contrast staining after ultrathin, biodegradable polymer sirolimus-eluting stent implantation: findings from optical coherence tomography and coronary angioscopy An optimized segmentation and quantification approach in microvascular imaging for OCTA-based neovascular regression monitoring Postdoctoral Research Fellowship at Nanyang Technological University, Singapore The Macular Choriocapillaris Flow in Glaucoma and Within-Day Fluctuations: An Optical Coherence Tomography Angiography Study Impact of Optical Coherence Tomography Imaging on Decision-Making During Percutaneous Coronary Intervention in Patients Presented With Acute Coronary Syndromes Time course of collateral vessel formation after retinal vein occlusion visualized by OCTA and elucidation of factors in their formation Peripapillary Retinal Nerve Fiber Layer and Ganglion Cell-Inner Plexiform Layer Changes on Optical Coherence Tomography in Patients with Unilateral Hypertensive Cytomegalovirus Anterior Uveitis Utility of spectral domain OCT in differentiating optic disc drusen from papilledema in children