A method to visualize, display, analyze and quantify angiography, perfusion, and the change in angiography and perfusion in real time, is provided. This method captures image data sequences from indocyanine green near infra-red fluorescence imaging used in a variety of surgical procedure applications, where angiography and perfusion are critical for intraoperative decisions.

To generate a medical image with high visibility in fluorescence observation. A medical image generation apparatus (100) according to the present application includes an acquisition unit (131), a calculation unit (132), and a generation unit (134). An acquisition unit (131) acquires a first medical image captured with fluorescence of a predetermined wavelength and a second medical image captured with fluorescence of a wavelength different from the predetermined wavelength. A calculation unit (132) calculates a degree of scattering, indicating a degree of blurring of fluorescence of a living body, included in the first medical image and the second medical image acquired by the acquisition unit (131). A generation unit (134) generates an output image on the basis of at least one of the degrees of scattering calculated by the calculation unit (132).

Methods and systems for determining a plurality of sequences of nucleic acid (e.g., DNA) molecules in a sequencing-by-synthesis process are provided. In one embodiment, the method comprises obtaining images of fluorescent signals obtained in a plurality of synthesis cycles. The images of fluorescent signals are associated with a plurality of different fluorescence channels. The method further comprises preprocessing the images of fluorescent signals to obtain processed images. Based on a set of the processed images, the method further comprises detecting center positions of clusters of the fluorescent signals using a trained convolutional neural network (CNN) and extracting, based on the center positions of the clusters of fluorescent signals, features from the set of the processed images to generate feature embedding vectors. The method further comprises determining, in parallel, the plurality of sequences of DNA molecules using the extracted features based on a trained attention-based neural network.

Provided is a fluorescence reader that uses two excitation channels and can read up to seven different fluorescent dyes in a single run. Each excitation channel has one light source and one single excitation filter and one dichroic mirror. One excitation channel is capable of exciting multiple fluorescent dyes and can be used to distinguish multiple dyes in combination with multiple emission filters. The excitation channels are driven by a motor that can automatically switch the two excitation channels for taking images of up to seven different fluorescent dyes. An algorithm to calibrate the crosstalk between different fluorescent dyes is also provided. Also provided is a method for analyzing digital PCR data using a ratio of two fluorescence emission readings.

In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a plurality of sets of images associated with a sample treated with fluorescence in situ hybridization (FISH) probes. Each image from that set of images is associated with a different focal length using a fluorescence microscope. Each FISH probe can selectively bind to a unique location on chromosomal DNA in the sample. The code further causes the processor to identify cell nuclei in the images. The code further causes the processor to apply a convolutional neural network (CNN) to each set of images. The CNN is configured to identify a probe indication from a plurality of probe indications for that set of images. The code further causes the processor to identify the sample as containing circulating tumor cells.

This invention provides a method to optimize an x-ray beam for more than one structure within the field of view. The preferred embodiment comprises a modular construction of a collimator comprising multiple materials of varying thickness. A first attenuation is performed by the first portion of the collimator to optimize a first anatomic feature and a second attenuation is performed by the second portion of the collimator to optimize a second anatomic feature.

One aspect of the present disclosure relates to a generation method for a training dataset, comprising: capturing, by one or more processors, a target object to which a marker unit recognizable under a first illumination condition is provided; and acquiring, by the one or more processors, a first image where the marker unit is recognizable and a second image obtained by capturing the target object under a second illumination condition.

The disclosure concerns a method for assessing muscular dystrophy-linked protein expression in muscle fibers using digital image analysis of tissue. The method relates to assessing disease severity in individuals with muscular dystrophy. Muscle tissue samples are obtained from patients submitted for evaluation and processed to produce tissue sections mounted on glass slides which have been stained for a muscular dystrophy-linked protein. Digital images of the stained tissue sections are generated and analyzed by applying an algorithm process implemented by a computer to the images. The algorithm process extracts the morphometric and staining features of the muscular dystrophy-linked protein staining in the tissue, and parameters relating to these features are used to score the disease status for each patient submitted for evaluation. The score of disease status is ultimately used to infer disease severity, monitor the efficacy of a therapeutic approach, or select patients as candidates for a therapeutic approach.

Method for detecting spatial coupling comprising the steps of: a. providing a set of data, b. identifying and segmenting a first and a second sets of objects of interest, wherein the objects of the second set are assimilated to punctual objects, c. determining, using a level set function, an expected number of objects of the second set present within a specified range of distances to at least one given object of the first set in case there were no interactions between said at least one given object of the first set and the objects of the second set, d. determining, using a level set function, an actual number of objects of the second set within the same range of distances to the at least one given object of the first set, and e. comparing said expected amount and said determined amount.

Disclosed is a computer-implemented method of overlaying a representation of a medical instrument over a two-dimensional medical image. It finds at least one feature point along a detection line which is defined relative to the medical instrument in the medical image, calculates a geometrical quantity based on this feature point and adds the geometrical quantity to the two-dimensional medical image.