Case Western Reserve University

David Prabhu

David L Wilson

Chaitanya Kolluru

Yazan Gharaibeh

Hao Wu

Hiram G Bezerra

Juhwan Lee

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Plaque segmentation in intravascular optical coherence tomography (OCT) images using deep learning

Embodiments discussed herein facilitate classification of vascular plaque. One example embodiment can: access vascular imaging data comprising one or more slices, wherein each slice comprises a plurality of A-lines of that slice; for each A-line of the plurality of A-lines of each slice of the one or more slices: extract one or more features for that A-line, wherein the one or more features for that A-line comprise at least one of: one or more features extracted from that A-line, one or more features extracted from the slice comprising that A-line, or one or more features extracted from the vascular imaging data; provide the one or more features for that A-line to at least one classifier; and generate a classification of that A-line via the at least one classifier, wherein the classification of that A-line is one of fibrocalcific, fibrolipidic, or other.

Assessment of arterial calcifications

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Dublin, OH, United States of America

David Prabhu