A method for generating an image of a region of interest in a biological sample comprising the steps of: generating a first image including the region of interest of the biological sample having undergone a first protocol but not a second protocol; and generating a second image including the region of interest of the biological sample after having undergone a second protocol; wherein the region of interest is smaller than said sample. Also provided is a method of analyzing a biological sample, comprising providing an image of the biological sample according to the method for generating an image of a region of interest in a biological sample, and analyzing the biological sample from the image. Further provided are system and kit that comprise the means for executing the novel methods.

In one embodiment, a method of machine learning and/or image processing for spectral object classification is described. In another embodiment, a device is described for using spectral object classification. Other embodiments are likewise described.

The method is for dividing dark objects, substructures and background of an image from an electron microscope into segments by analyzing pixel values. The segments are transformed and aligned so that the transformed objects, sub-structures and background are meaningfully comparable. The transformed segments are clustered into classes which are used for ontological investigation of samples that are visualized by using electron microscopy. A triangle inequality comparison can be used to further cluster groups of objects to transfer understanding from different interactions between objects and to associate interactions with each other.

To provide a microscope device capable of efficiently or appropriately acquiring an image of a specific region of a living tissue.

The present technology provides a microscope device including: a first imaging element that images a target including a body tissue and acquires image data; and a second imaging element that images the target at a magnification different from a magnification of the first imaging element and acquires image data, in which the first imaging element includes a determination unit that determines a feature related to the target on the basis of the image data, and the second imaging element is controlled on the basis of a result of the determination. Furthermore, the present technology also provides an image acquisition system including the microscope device. Furthermore, the present technology also provides an image acquisition method performed in the microscope device.

A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the: one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.

Disclosed herein are charged particle microscopy (CPM) support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a CPM support apparatus may include: first logic to cause a CPM to generate a single image of a first portion of a specimen; second logic to generate a first mask based on one or more regions-of-interest provided by user annotation of the single image; and third logic to train a machine-learning model using the single image and the one or more regions-of-interest. The first logic may cause the CPM to generate multiple images of corresponding multiple additional portions of the specimen, and the second logic may, after the machine-learning model is trained using the single image and the one or more regions-of-interest, generate multiple masks based on the corresponding images of the additional portions of the specimen using the machine-learning model without retraining.

A system and method of screening biological specimens by at least one processor may include receiving a sample image depicting a biological specimen; applying a machine-learning (ML) based autoencoder on the sample image, wherein said autoencoder is trained to generate a reconstructed version of the sample image, via a latent feature vector; associating a latent feature of the latent feature vector to a corresponding visual phenotype of the biological specimen; and screening the biological specimen based on said association. Embodiments of the invention may subsequently modify a value of the latent feature to produce a vector set, comprising a plurality of latent feature vectors; apply a decoder portion of the autoencoder on the vector set, to produce a corresponding reconstructed image set, representing evolution or amplification of a visual phenotype of the biological specimen; and associate the latent feature to the visual phenotype based on the reconstructed image set.

A method for evaluating material properties includes an image processing for evaluation step, a material properties prediction step, and an evaluation step. The image processing for evaluation step includes scanning one or more images for evaluation of a material to be evaluated, creating a low-gradation image for evaluation by lowering gradation of the image for evaluation, and creating a virtual image by processing the low-gradation image for evaluation. The material properties prediction step includes extracting features for evaluation from the low-gradation image for evaluation, predicting a first material property of the material to be evaluated from the features for evaluation through a regression model, extracting a virtual-image feature from the virtual image, and predicting a second material property of the material to be evaluated from the virtual-image features through the regression model. The evaluation step is for comparing the first material property with the second material property.

A computing device, method, system, and instructions in a non-transitory computer-readable medium for performing image analysis on 3D microscopy images to predict localization and/or labeling of various structures or objects of interest, by predicting the location in such images at which a dye or other marker associated with such structures would appear. The computing device, method, and system receives sets of 3D images that include unlabeled images, such as transmitted light images or electron microscope images, and labeled images, such as images captured with fluorescence tagging. The computing device trains a statistical model to associate structures in the labeled images with the same structures in the unlabeled light images. The processor further applies the statistical model to a new unlabeled image to generate a predictive labeled image that predicts the location of a structure of interest in the new image.

An image processing method according to the invention includes obtaining a ground truth image teaching a cell region occupied by a cell in an original image for each of a plurality of the original images obtained by bright-field imaging of the cell, generating a reverse image by reversing luminance of the original image at least for the cell region based on each original image, and constructing a classification model by performing machine learning using a set of the original image and the ground truth image corresponding to the original image and a set of the reverse image and the ground truth image corresponding to the original image as a basis of the reverse image respectively as training data.