Within the field of elastography, a new approach analyzes the limiting case of shear waves established as a reverberant field. In this framework, it is assumed that a distribution of shear waves exists, oriented across all directions in 3D (e.g. 2D space+time). The simultaneous multi-frequency application of reverberant shear wave fields can be accomplished by applying an array of external sources that can be excited by multiple frequencies within a bandwidth, for example 50, 100, 150, . . . 500 Hz, all contributing to the shear wave field produced in the liver or other target organ. This enables the analysis of the dispersion of shear wave speed as it increases with frequency, indicating the viscoelastic and lossy nature of the tissue under study. Furthermore, dispersion images can be created and displayed alongside the shear wave speed images. Studies on breast and liver tissues using the multi-frequency reverberant shear wave technique, employing frequencies up to 700 Hz in breast tissue, and robust reverberant patterns of shear waves across the entire liver and kidney in obese patients are reported. Dispersion images are shown to have contrast between tissue types and with quantitative values that align with previous studies.

Embodiments relate to extracting blood vessel geometry from one or more optical coherent tomography (OCT) images for use in analyzing biological structures for diagnostic and therapeutic applications for diseases that can be detected by vascular changes in the retina. An OCT image refers generally to one or more images of any dimension obtained using any one or combination of OCT techniques. Some embodiments include a method of identifying a region of interest of a retina from a plurality of retinal blood vessels in at least one optical coherence tomography (OCT) image of at least a portion of the retina. Some embodiments include a method of distinguishing between a plurality of retinal layers from vessel morphology information of retinal blood vessels in at least one optical coherence tomography (OCT) image of at least a portion of the retina.

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.

A method and device for multimodal imaging of dermal and mucosal lesions. The method includes using at least two imaging modalities from which one is a 3D scan of the lesion, and, additionally providing information on the distance and angulation between scanning device and the dermis or mucosa and mapping at least the second modality over the 3D data.

A computer is caused to perform processing of: acquiring a plurality of medical images generated based on signals detected by a catheter inserted into a lumen organ while the catheter is moving a sensor along a longitudinal direction of the lumen organ, the lumen organ including a main trunk, a side branch branched from the main trunk, and a bifurcated portion of the main trunk and the side branch; and recognizing a main trunk cross-section, a side branch cross-section, and a bifurcated portion cross-section by inputting the acquired medical images into a learning model configured to recognize the main trunk cross-section, the side branch cross-section, and the bifurcated portion cross-section.

Aspects of the disclosure relate to systems, methods, and algorithms to train a machine learning model or neural network to classify OCT images. The neural network or machine learning model can receive annotated OCT images indicating which portions of the OCT image are blocked and which are clear as well as a classification of the OCT image as clear or blocked. After training, the neural network can be used to classify one or more new OCT images. A user interface can be provided to output the results of the classification and summarize the analysis of the one or more OCT images.

The present disclosure, in some embodiments, relates to a method of predicting stent expansion. The method includes accessing a pre-stent intravascular image of a blood vessel of a patient and segmenting the pre-stent intravascular image to identify a lumen and a calcification lesion. A plurality of features are extracted from one or more of the lumen and the calcification lesion. A regression model is applied to one or more of the plurality of features to determine a minimum stent expansion metric (mSEM). The mSEM indicating how much a stent will expand after implantation. The mSEM is used to generate a classification of the blood vessel as an under-expanded area or a well-expanded area.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A processor of an ophthalmologic image processing device acquires an ophthalmologic image photographed by an ophthalmologic image photographing device. The processor inputs the ophthalmologic image into a mathematical model trained by a machine learning algorithm to acquire a result of an analysis relating to at least one of a specific disease and a specific structure of a subject eye. The processor acquires information of a distribution of weight relating to an analysis by a mathematical model, as supplemental distribution information, for which an image area of the ophthalmologic image input into the mathematical model is set as a variable. The processor sets a part of the image area of the ophthalmologic image, as an attention area, based on the supplemental distribution information. The processor acquires an image of a tissue including the attention area among a tissue of the subject eye and displays the image on a display unit.

A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.