The application relates to devices and methods for analysing a colonoscopy video or a portion thereof, and for assessing the severity of ulcerative colitis in a subject by analysing a colonoscopy video obtained from the subject. Analysing a colonoscopy video comprises using a first deep neural network classifier to classify image data from the subject colonoscopy video or portion thereof into at least a first severity class (more severe endoscopic lesions) and a second severity class (less severe endoscopic lesions), wherein the first deep neural network has been trained at least in part in a weakly supervised manner using training image data from a plurality of training colonoscopy videos, the training image data comprising multiple sets of consecutive frames from the plurality of training colonoscopy videos, wherein frames in a set have the same severity class label. Devices and methods for providing a tool for analysing colonoscopy videos are also described.

In a method for processing microscope images, at least one microscope image is provided as input image for an image processing algorithm. An output image is created from the input image by means of the image processing algorithm. The creation of the output image comprises adding low-frequency components for representing solidity of image structures of the input image to the input image, wherein the low-frequency components at least depend on high-frequency components of these image structures and wherein high-frequency components are defined by a higher spatial frequency than low-frequency components. A corresponding computer program and microscope system are likewise described.

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.

A microscopy includes multiple cameras working together to capture image data of a sample having a group of organisms distributed over a wide area, under the influence of an excitation instrument. A first processor is coupled to each camera to process the image data captured by the camera. Outputs from the multiple first processors are aggregated and streamed serially to a second processor for tracking the organisms. The presence of the multiple cameras capturing images from the sample, configured with 50% or more overlap, can allow 3D tracking of the organisms through photogrammetry.

Systems and methods are described for the fully automatic, template-free locating and extracting of a plurality of two-dimensional projections of particles in a micrograph image. A set of reference images is automatically assembled from a micrograph image by analyzing the image data in each of a plurality of partially overlapping windows and identifying a subset of windows with image data satisfying at least one statistic criterion compared to other windows. A normalized cross-correlation is then calculated between the image data in each reference image and the image data in each of a plurality of query image windows. Based on this cross-correlation analysis, a plurality of locations in the micrograph is automatically identified as containing a two-dimensional projection of a different instance of the particle of the first type. The two-dimensional projections identified in the micrograph are then used to determine the three-dimensional structure of the particle.

A computational microscope and a method for its operation are disclosed. In some embodiments, the microscope maps points on a sample to point in an intensity pattern on a one-to-many basis. The microscope utilizes illumination angle coding, polarization coding, amplitude coding, and phase coding to capture more information than prior art computational microscopes. Although the resulting intensity patterns are not human-interpretable images of the sample, they contain more information about the sample, by virtue of the aforementioned coding techniques, than is captured by prior-art microscopes. Machine-learning algorithms, such as neural networks, are used to analyze the intensity patterns and extract useful information, such as cellular events or cell behavior.

The present disclosure describes techniques of improving video matting. The techniques comprise extracting features from each frame of a video by an encoder of a model, wherein the video comprises a plurality of frames; incorporating, by a decoder of the model, into any particular frame temporal information extracted from one or more frames previous to the particular frame, wherein the particular frame and the one or more previous frames are among the plurality of frames of the video, and the decoder is a recurrent decoder; and generating a representation of a foreground object included in the particular frame by the model, wherein the model is trained using segmentation dataset and matting dataset.

A system for identifying microbial agents such as virus particles in a sample. The system includes at least one processing unit for identifying in an electron micrograph obtained from the sample a darker region and identifying virus particles within the darker region. The system can optionally include an electron microscope, a sample collector and sample treatment chamber.

A specimen processing system includes a plate for supporting a specimen system, wherein the specimen system includes a container and a specimen contained therein. The specimen processing system further includes a camera disposed above the plate and configured to generate images of the specimen system, a light source disposed beneath the plate for radiating light towards the plate, a light stop for blocking a portion of the light from reaching the specimen system to produce darkfield illumination of the specimen at the camera, and one or more processors electronically coupled to the camera and configured to track a position of the specimen within the specimen container during a specimen processing protocol based on the images.