Systems and methods for classification of arterial image regions and features thereof

Abstract

In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 62 / 791,876 (filed January 13, 2019), the entire disclosure of which is hereby incorporated by reference. technical field [0003] This application relates in part to imaging arteries and segmenting and characterizing their components. In particular, in some embodiments, the present application relates to the application of machine learning to characterize and / or classify arterial tissue and related arterial regions and features of interest. Background technique [0004] Optical coherence tomography (OCT) is an interferometric imaging technique with wide applications in ophthalmology, cardiology, gastroenterology and other medical fields. The ability to view subsurface structures at high resolution through small-diameter fiber optic probes makes OCT particularly useful for minimally invasive imaging of internal tissues and or...

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