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.

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.

A benign tumor development trend assessment system includes an image outputting device and a server computing device. The image outputting device outputs first/second images captured from the same position in a benign tumor. The server computing device includes an image receiving module, an image pre-processing module, a target extracting module, a feature extracting module and a trend analyzing module. The image receiving module receives the first/second images. The image pre-processing module pre-processes the first/second images to obtain first/second local images. The target extracting module automatically detects and delineates tumor regions from the first/second local images to obtain first/second region of interest (ROI) images. The feature extracting module automatically identifies the first/second ROI images to obtain at least one first/second features. The trend analyzing module analyzes the first/second features to obtain a tumor development trend result.

A fluorescence microscopy inspection system includes light sources able to emit light that causes a specimen to fluoresce and light that does not cause a specimen to fluoresce. The emitted light is directed through one or more filters and objective channels towards a specimen. A ring of lights projects light at the specimen at an oblique angle through a darkfield channel. One of the filters may modify the light to match a predetermined bandgap energy associated with the specimen and another filter may filter wavelengths of light reflected from the specimen and to a camera. The camera may produce an image from the received light and specimen classification and feature analysis may be performed on the image.

A technique and device for managing blocks of commands intended for a microscopy imaging device configured to acquire images of a sample. Each block of commands includes driving commands serving to drive a plurality of functional modules of the imaging device. Each command is defined by at least one acquisition parameter. The technique includes executing a first, predefined block of commands to acquire first images, and upon positive verification, by image analysis of a stop condition upon executing the first block, stopping the first block to execute a second predefined block of commands to acquire second images, the commands of the second block being defined by at least one second acquisition parameter, dynamically defined depending on the image analysis.

Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care.

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.

The technology disclosed herein relates to an imaging device. In some embodiments the imaging device has a support plate defining an object plane. A housing surrounds the object plane across the support plate. A first reflector plane is within the housing and in reflective communication with the object plane. The first reflector plane is 68.0° to 70.0° from the object plane. A second reflector plane within the housing and in reflective communication with the object plane. The second reflector plane is 68.0° to 70.0° from the object plane. Other embodiments are also described.

Various examples of the disclosure relate to aspects associated with training a machine-learned algorithm configured to count cells in a microscopy image or to determine a degree of confluence of the cells.

The invention relates to method and system configured for material analysis and mineralogy. At least one image based on first emission from a sample is provided. First spectra of the sample based on second emissions from the second scan locations of the image are provided. A confidence score is calculated for every first spectrum, and second scan location(s) with confidence score(s) below a threshold value are selected. Second emissions from the selected second scan location(s) are acquired to provide new image and determine new second scan locations within the respective new image.