Disclosed herein include embodiments of a system, a device, and a method for sorting a plurality cells of a sample. A plurality of raw images comprising pixels of complex values in a frequency space can be generated from a plurality of channels of fluorescence intensity data of fluorescence emissions of fluorophores, the fluorescence emissions being elicited by fluorescence imaging using radiofrequency-multiplexed excitation in a temporal space. Spectral unmixing can be performed on the raw images prior to a sorting decision being made.

Multiplexed fluorescent imaging which is essential for finding out how various biomolecules are spatially distributed in cells or tissues is disclosed. The present disclosure may obtain 10 or more different biomolecule images with one labeling and imaging by newly designing selection of fluorophores, detection spectral ranges, and signal unmixing algorithm. The present disclosure is a blind unmixing technology for unmixing an image without an emission spectrum of fluorophore, and in this technology, 4 pairs of fluorophores are used, and each pair consists of two fluorophores in which emission spectra are overlapped. Each pair of fluorophores is strongly excited by only one excitation laser. Two images with different detection spectral ranges are obtained for each pair, and two images are unmixed via mutual information minimization without fluorophore emission spectrum information. Two images also may be unmixed via Gram-Schmidt orthogonalization and fluorescence measurement based unmixing. This signal unmixing is repeated for each pair of fluorophores. Furthermore, a total of 10 or more fluorophores may be simultaneously used by adding two large stoke's shift fluorophores emitting light in wavelength ranges that does not overlap with the emission spectra of the above 8 fluorophores.

A high-resolution microendoscope system includes a light source, a fiber optic bundle configured to transmit light from the light source to a sample, a disc configured to receive light returned from the sample, the disc having spaced apart segments, the spaced-apart segments being at least one of openings and transparent portions, a first camera configured to capture a first image based at least in part on light passing through the disc, and a second camera configured to capture a second image based at least in part on light reflected from the disc.

A non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process, an information processing apparatus, and a model generation method that outputs complication information for a medical treatment. The process includes acquiring a medical image obtained by imaging a lumen organ of a patient before treatment, inputting the acquired medical image into a trained model so as to output complication information on a complication that is likely to occur after the treatment when the medical image is received, and outputting the complication information. Preferably, complication information including a type of the complication that is likely to occur and a probability value indicating an occurrence probability of the complication of the type is output.

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.

Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.

In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for selectively presenting images captured by confocal laser endomicroscopy (CLE) are provided. In some embodiments, a method comprises: receiving images captured by a CLE device during brain surgery; providing the images to a convolution neural network (CNN) trained using at least a plurality of images of brain tissue captured by a CLE device and labeled diagnostic or non-diagnostic; receiving an indication, from the CNN, likelihoods that the images are diagnostic images; determining, based on the likelihoods, which of the images are diagnostic images; and in response to determining that an image is a diagnostic image, causing the image to be presented during the brain surgery.

Systems and methods for predicting a patient response to various agents and/or combinations of agents using ex vivo dosing and imaging are disclosed. In one example, a method of determining treatment efficacy includes analyzing a solid cell culture over time, e.g., first and second responses to a solid cell culture to respective treatments may be compared to determine a treatment efficacy of each treatment. Systems and methods for applying the treatments to the cell culture and analyzing the cell culture and efficacy are disclosed.

A method performed by a computing system comprises receiving a fluoroscopic image of a patient anatomy while a portion of a medical instrument is positioned within the patient anatomy. The fluoroscopic image has a fluoroscopic frame of reference. The portion has a sensed position in an anatomic model frame of reference. The method further comprises identifying the portion in the fluoroscopic image and identifying an extracted position of the portion in the fluoroscopic frame of reference using the identified portion in the fluoroscopic image. The method further comprises registering the fluoroscopic frame of reference to the anatomic model frame of reference based on the sensed position of the portion and the extracted position of the portion.

Examples relate to systems, methods and computer programs for a microscope system and for determining a transformation function, and to a corresponding microscope system. The system for the microscope system comprises one or more processors and one or more storage devices. The system is configured to obtain first imaging sensor data from a first imaging sensor of a microscope of the microscope system and second imaging sensor data from a second imaging sensor of the microscope, the first imaging sensor data comprises sensor data on light sensed in a first plurality of mutually separated wavelength bands. The second imaging sensor data comprises sensor data on light sensed in a second plurality of mutually separated wavelength bands. The wavelength bands of the first plurality of mutually separated wavelength bands or of the second plurality of mutually separated wavelength bands are wavelength bands that are used for fluorescence imaging. The system is configured to generate a composite color image based on the first imaging sensor data and based on the second imaging sensor data. The composite color image is based on a plurality of color channels. The composite color image is generated using a transformation function to define a transformation to be performed between the imaging sensor data and the composite color image, such that the composite color image is generated using sensor data on light sensed in each wavelength band of the first and second plurality of mutually separated wavelength bands.