An atomic force microscope includes a sample stage on which a sample is placed, a cantilever including a probe tip, a laser radiating a laser beam to the cantilever, a photodetector receiving a laser beam reflected from the cantilever, a first camera photographing the sample and the cantilever, a second camera photographing the cantilever and the spot of the laser beam, and a processor electrically connected to the first and second cameras and the photodetector to process data acquired by the first and second cameras and the photodetector. An operation method of the atomic force microscope includes detecting the positions of the cantilever and the sample using the first camera, adjusting the position of the sample, detecting the positions of the laser and the cantilever using the second camera, aligning the laser, detecting the position of the laser beam using the photodetector, and aligning the position of the photodetector.

A system and method to segment an image captured from an image capture device of a high content imaging system includes an image acquisition module that receives the image captured by the image capture device. A coarse object detection module develops a coarse segmented image, wherein each pixel of the coarse segmented image is associated with a corresponding pixel in the captured image and is identified as one of an object pixel and a background pixel. A marker identification module selects at least one marker pixel from the pixels of the coarse segmented image, wherein each marker pixel is one of a contiguous group of object pixels in the coarse segmented image that is furthest from a background pixel relative to neighboring pixels of the group. An object splitting module that comprises a plurality of processors operating in parallel that associates each object pixel of the coarse segmented image with a marker pixel, wherein a distance based metric between the object pixel and the marker pixel is less than the distance based metric between the object pixel and any other marker pixel in the coarse segmented image.

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.

Disclosed is an image quality evaluation method for a digital pathology system according to the present invention. The image quality evaluation method includes receiving a digital slide image by an image quality evaluation unit; dividing the digital slide image into a plurality of blocks by the image quality evaluation unit; analyzing the plurality of blocks to extract a foreground; calculating a blur for the extracted foreground; calculating brightness distortion for the extracted foreground; calculating contrast distortion for the extracted foreground; and evaluating the overall quality of the digital slide image using the blur, the brightness distortion, and the contrast distortion by the image quality evaluation unit.

A sample observation device includes: an emission optical system that emits planar light to a sample on an XZ plane; a scanning unit that scans the sample in a Y-axis direction so as to pass through an emission surface of the planar light; an imaging optical system that has an observation axis inclined with respect to the emission surface and forms an image of observation light generated in the sample; an image acquisition unit that acquires a plurality of pieces of XZ image data corresponding to an optical image of the observation light; and an image generation unit 8 that generates XY image data based on the plurality of pieces of XZ image data. The image generation unit extracts an analysis region of the plurality of pieces of XZ image data acquired in the Y-axis direction, integrates brightness values of at least the analysis region in a Z-axis direction to generate X image data, and combines the X image data in the Y-axis direction to generate the XY image data.

A metrology method for use in determining one or more parameters of a three-dimensional patterned structure, the method including performing a fitting procedure between measured TEM image data of the patterned structure and simulated TEM image data of the patterned structure, determining a measured Lamellae position of at least one measured TEM image in the TEM image data from a best fit condition between the measured and simulated data, and generating output data indicative of the simulated TEM image data corresponding to the best fit condition to thereby enable determination therefrom of the one or more parameters of the structure.

Methods and apparatus distinguish invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS). One example apparatus includes a set of circuits, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of circuits includes a classification circuit that generates an invasiveness classification for a diagnostic 3D radiological image, a training circuit that trains the classification circuit to identify a texture feature associated with IA, an image acquisition circuit that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification circuit, and a prediction circuit that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training circuit trains the classification circuit using a set of 3D histological reconstructions combined with the set of 3D radiological images.

A method for refining label information, which is performed by at least one computing device is disclosed. The method includes acquiring a pathology slide image including a plurality of patches, inferring a plurality of label information items for the plurality of patches included in the acquired pathology slide image using a machine learning model, applying the inferred plurality of label information items to the pathology slide image, and providing the pathology slide image applied with the inferred plurality of label information items to an annotator terminal.

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.

A method for generating a color-faithful extended-depth-of-field (EDF) image from a color volume of 2D images acquired at different focal depths using a microscope. The method involves: generating a grayscale volume; applying invertible color-to-grayscale transformation to the volume; applying wavelet transform to the grayscale volume to obtain a 3D wavelet-coefficient-matrix (WCM); selecting wavelet coefficients using a coefficient selection rule; generating a 2D-WCM and a 2D coefficient-map (CM); applying inverse transformation of the wavelet transform to the 2D-WCM to obtain a 2D grayscale EDF image; generating a 2D color-composite(CC) image; applying inverse transformation of the color-to-grayscale transformation to the 2D grayscale EDF image to obtain a 2D color EDF image; converting the 2D-CC image and the 2D color EDF image into a color space including chromaticity and intensity component(s); and concatenating, chromaticity component(s) of the 2D-CC image and intensity component(s) of the 2D color EDF image, to obtain a color-faithful EDF image.