1. Articles from Yazan Gharaibeh

    1-4 of 4
    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. 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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    3. Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets

      Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets

      We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous studies (e.g., optical attenuation and A-line peaks). In addition, we included vascular lumen morphology and three-dimensional (3-D) digital edge and texture features. Classification methods were developed using expansive datasets (∼7000  images), consisting of both clinical in-vivo images and an ex-vivo dataset, which was validated using 3-D cryo-imaging/histology. Conditional random field was used to perform 3-D classification noise cleaning of classification results. We tested various multiclass approaches, classifiers ...

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    4. Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images

      Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images

      We develop neural-network-based methods for classifying plaque types in clinical intravascular optical coherence tomography (IVOCT) images of coronary arteries. A single IVOCT pullback can consist of >500 microscopic-resolution images, creating both a challenge for physician interpretation during an interventional procedure and an opportunity for automated analysis. In the proposed method, we classify each A-line, a datum element that better captures physics and pathophysiology than a voxel, as a fibrous layer followed by calcification (fibrocalcific), a fibrous layer followed by a lipidous deposit (fibrolipidic), or other. For A-line classification, the usefulness of a convolutional neural network (CNN) is compared with that ...

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    1-4 of 4
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  2. Topics in the News

    1. (4 articles) Case Western Reserve University
    2. (4 articles) David L. Wilson
    3. (4 articles) Hiram G. Bezerra
    4. (1 articles) Ospedali Riuniti di Bergamo
    5. (1 articles) Cleveland Clinic
    6. (1 articles) Giulio Guagliumi
    7. (1 articles) Fujian Normal University
    8. (1 articles) UCLA
    9. (1 articles) Saarland University
    10. (1 articles) Srinivas R. Sadda
    11. (1 articles) Yuhua Zhang
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    Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features Accuracy of optical coherence tomography imaging in assessing aneurysmal remnants after flow diversion Subtype-differentiated impacts of subretinal drusenoid deposits on photoreceptors revealed by adaptive optics scanning laser ophthalmoscopy Can ocular changes be detected early in children and adolescents with type 1 diabetes mellitus without retinopathy by using optical biometry and optical coherence tomography? CD4+/CD8+ ratio positively correlates with coronary plaque instability in unstable angina pectoris patients but fails to predict major adverse cardiovascular events Optical coherent tomography to evaluate the degree of inflammation in a mouse model of colitis Circumpapillary collateral vessel development in iatrogenic central retinal artery occlusion observed using OCT angiography Deep learning with 4D spatio-temporal data representations for OCT-based force estimation Assessment of retinal vascular network in amnestic mild cognitive impairment by optical coherence tomography angiography