In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a plurality of sets of images associated with a sample treated with fluorescence in situ hybridization (FISH) probes. Each image from that set of images is associated with a different focal length using a fluorescence microscope. Each FISH probe can selectively bind to a unique location on chromosomal DNA in the sample. The code further causes the processor to identify cell nuclei in the images. The code further causes the processor to apply a convolutional neural network (CNN) to each set of images. The CNN is configured to identify a probe indication from a plurality of probe indications for that set of images. The code further causes the processor to identify the sample as containing circulating tumor cells.

An information processing device (200A, 200B, and 200C) according to the present disclosure includes a control unit (220, 220B, and 220C). The control unit (220, 220B, and 220C) acquires a captured image of a target imaged by a sensor. The captured image is an image obtained from reflected light of light emitted to the target from a plurality of light sources arranged at different positions, respectively. The control unit (220, 220B, and 220C) extracts a flat region from the captured image based on a luminance value of the captured image. The control unit (220, 220B, and 220C) calculates shape information regarding a shape of a surface of the target based on information regarding the sensor and the flat region of the captured image.

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 system and method for processing multi-modal microscopy imaging data on small-scale computer architecture which avoids restrictive manufacturer data formats and APIs. The system and method leverage a web-based application made available to microscopy instrument control hardware by which direct visual output of the control hardware is captured and transmitted to an edge computing device for processing by one or more inference models in parallel to construct a composite hyperimage.

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.

A margin assessment method is provided. Under cooperation of harmonic generation microscopy (HGM) and a deep learning method, the margin assessment method can instantaneously and digitally determine whether a 3D image group generated by an HGM imaging system is a malignant tumor or the surrounding normal skin, so as to assist in determining margins of a lesion.

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.

Irradiation light in a visible light region is irradiated to a sample while switching irradiation of infrared light IR having a wavelength that corresponds to the infrared absorption spectrum of an observation target material included in the sample between a first state and a second state. A first image and a second image are generated based on the phase distribution, the intensity distribution, and the polarization direction distribution of the light including the irradiation light that has passed through the sample in synchronization with the switching of the infrared light IR irradiation between the first state and the second state. Subsequently, an output image is generated so as to represent one from among the position, size, and shape based on the difference and/or ratio with respect to the pixel values for each pixel between the first image and the second image.

Apparatus and methods are described use with a bodily sample that contains cells. A microscope (24) is focused, such that a focal plane of the microscope (24) at least approximately coincides with a level at which at least some cells belonging to the sample are at least partially disposed. At least one on-focus microscopic image of the sample, while the focal plane of the microscope (24) approximately coincides with the level. The microscope (24) is focused such that the focal plane of the microscope is offset with respect to the level, at least one off-focus microscopic image of the sample is acquired, while the focal plane of the microscope (24) is offset with respect to the level. A property of at least a portion of the sample is determined, at least partially based upon the on-focus and off-focus images. Other applications are also described.

A system and method for analyzing bodily fluid include a sample holder holding a bodily fluid sample, an image capture device generating an image of the bodily fluid sample comprising a plurality of fields of view. An image processor is programmed to determine a biofilm in the bodily fluid sample from the image, determine a biofilm area or volume within each of the plurality of fields of view to form a plurality of biofilm areas, determine a total biofilm area or total biofilm volume by adding the plurality of biofilm areas, determine a first value corresponding to a comparison of the total biofilm area or the total biofilm volume and a total volume of the bodily fluid sample, and classify the first value into a classification. An analyzer, using the classification, displays an indicator on a display for indicating the classification of the biofilm within the bodily fluid sample.