A method for determining geometrical parameters of a soft contact lens comprises the steps of providing an OCT imaging device comprising an OCT light source; providing a soft contact lens arranging the soft contact lens relative to the OCT imaging device so light coming from the OCT light source impinges on the back surface of the soft contact lens; generating a three-dimensional OCT image of the soft contact lens; from the three-dimensional OCT image determining a plurality of edge points located on the edge of the soft contact lens, connecting adjacent ones of the edge points by individual straight lines; summing up the lengths of all individual straight lines to a length U of the approximated circumference of the soft contact lens; from the length U determining a diameter D of the lens according to D=U/π.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Aspects of the disclosure provide for methods, systems, and apparatuses, including computer-readable storage media, for lipid detection by identifying fibrotic caps in medical images of blood vessels. A method includes receiving one or more input images of a blood vessel and processing the one or more input images using a machine learning model trained to identify locations of fibrotic caps in blood vessels. The machine learning model is trained using a plurality of training images each annotated with locations of one or more fibrotic caps. A method includes identifying and characterizing fibrotic caps of lipid pools based on differences in radial signal intensities measured at different locations of an input image. A system can generate one or more output images having segments that are visually annotated representing predicted locations of fibrotic caps covering lipidic plaques.

A system that includes an interference device including a reference arm on which a reflective surface is arranged, where the interference device produces, at each point of an imaging field when the sample is placed on a target arm of the interference device, interference between a reference wave and a target wave obtained by backscattering of incident light waves by means of a voxel of a slice of the sample at a given depth; an acquisition device suitable for acquiring, at a fixed path length difference between the target arm and the reference arm, a temporal series of N two-dimensional interferometric signals resulting from the interference produced at each point of the imaging field; and a processing unit that calculates an image representing temporal variations in intensity between said N two-dimensional interferometric signals.

A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Retinal diseases, such as age-related macular degeneration (AMD), are the leading cause of blindness in the elderly population. Since no known cures are currently present, it is crucial to diagnose the condition in its early stages so that disease progression is monitored. Systems and methods for detecting and mapping the mechanical elasticity of retinal layers in the posterior eye are disclosed herein. A system including confocal shear wave acoustic radiation force optical coherence elastography (SW-ARF-OCE) is provided, wherein an ultrasound transducer and an optical scan head are co-aligned to facilitate in-vivo study of the retina. In addition, an automatic segmentation algorithm is used to isolate tissue layers and analyze the shear wave propagation within the retinal tissue to estimate mechanical stress on the retina and detect early stages of retinal diseases based on the estimated mechanical stress.

A method for identifying a type of choroidal neovascularization in a retina of a human eye is disclosed, the method comprising receiving, in a processor, optical coherence tomography data of the eye; generating, in the processor, volume segments of the eye using the optical coherence tomography data and a neural network; and identifying, in the processor, the type of choroidal neovascularization in the eye, using the volume segments, the type of choroidal neovascularization comprising one or more of the following: classic choroidal neovascularization and occult choroidal neovascularization.

An imaging system may include: a first light source configured to emit a first source spectrum of collimated light; a second light source configured to emit a second source spectrum of light; a probe head configured to direct the first source spectrum and the second source spectrum toward tissue in an oral cavity and to collect a first feedback spectrum of light and a second feedback spectrum of light; an interferometry sub-system to generate an optical feedback signal using the first source spectrum; at least one optical sensor array for receiving the optical feedback signal and the second feedback spectrum; and at least one programmable processor to generate: a first diagnostic image of the tissue using the optical feedback signal; a second diagnostic image of the tissue using the second feedback spectrum; and a third diagnostic image from a combination of the first diagnostic image and the second diagnostic image.

An ophthalmic apparatus of an aspect example includes an image acquiring unit, a corneal shape estimating processor, and a first image correcting processor. The image acquiring unit is configured to acquire an anterior segment image constructed based on data collected from an anterior segment of a subject's eye by optical coherence tomography (OCT) scanning. The anterior segment image includes a missing part corresponding to a part of a cornea. The corneal shape estimating processor is configured to estimate a shape of the missing part of a cornea image by analyzing the anterior segment image acquired by the image acquiring unit. The first image correcting processor is configured to correct distortion of the anterior segment image based at least on the shape of the missing part estimated by the corneal shape estimating processor.

An apparatus for recording and displaying a three-dimensional OCT video includes an OCT system and a data processing unit configured to combine OCT volumes recorded sequentially in time to form a combined OCT volume, to generate display data which facilitate the display of the combined OCT volume, to register a current OCT volume of the OCT volumes, to combine the registered current OCT volume with the previous combined OCT volume to generate an updated combined OCT volume, to carry out a check for combining the registered current OCT volume with the previous combined OCT volume and in respect of which volume regions of the current OCT volume have changed in comparison with the corresponding volume regions of the previous combined OCT volume, and only to combine those volume regions of the current OCT volume that have not changed with the corresponding volume regions of the previous combined OCT volume.