A measurement apparatus according to an embodiment of the present technology includes a light source, a filling portion, and a detector. The light source emits illumination light. The filling portion includes a first surface portion and a second surface portion which are provided on an optical path of the illumination light and are opposite to each other, the filling portion enabling a cavity between the first and second surface portions to be filled with liquid including a cell. The detector detects an interference fringe of the illumination light passing through the cavity, the interference fringe being caused by the liquid including the cell.

A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

Provided are a sample image analyzer and a corresponding method. The sample image analyzer includes: an object stage for supporting a sample carrier; an imaging device for capturing an image of an object in a sample on the sample carrier; a driving device for driving the object stage and the imaging device to move relative to each other; and a control device configured to control the driving device to deliver the sample carrier to a position below the imaging device, control the driving device to drive the object stage and the imaging device to move horizontally relative to each other, and to move vertically relative to each other, control the imaging device to capture, at least during the relative vertical movement, images of the object at different horizontal positions and at different vertical positions, and fuse the images of the object to obtain a target image of the object.

To optimize an imaging range in a depth direction in terms of a relationship with a resolution, an OCT apparatus includes a signal processor that determines a reflected light intensity distribution of an imaging object on the basis of a spectrum of a detected interference light. The signal processor performs spectrum conversion, having a conversion characteristic with which a light source spectrum is converted to a Gaussian distribution curve, on the spectrum of the interference light, and determines the reflected light intensity distribution by Fourier-transforming a spectrum resulting from the spectrum conversion. In the conversion characteristic, the light source spectrum and the Gaussian distribution curve have center wavelengths differing from each other.

An optical measurement system comprises a polarization beam splitter for dividing an incident beam into a reference beam and a measurement beam, a first beam splitter for reflecting the measurement beam to form a first reflected measurement beam, a spatial light modulator for modulating the first reflected measurement beam to form a modulated measurement beam, a condenser lens for focusing the modulated measurement beam to an object to form a penetrating measurement beam, an objective lens for converting the penetrating measurement beam into a parallel measurement beam, a mirror for reflecting the parallel measurement beam to form an object beam, a second beam splitter for reflecting the reference beam to a path coincident with that of the object beam, and a camera for receiving an interference signal generated by the reference beam and the object beam to generate an image of the object.

The apparati, methods, and computer program products disclosed herein can be used to nondestructively detect undissolved particles, such as glass flakes and/or protein aggregates, in a fluid in a vessel, such as, but not limited to, a fluid that contains a drug.

A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

An apparatus for nondestructive detection of transparent or reflective objects in a vessel includes an imager configured to acquire data that represent light reflected from spatial locations in the vessel as a function of time, a memory operably coupled to the imager and configured to store the data, and a processor operably coupled to the memory and configured to detect the objects based on the data by (i) identifying a respective maximum amount of reflected light, over time, for each location in the spatial locations based on the data representing light reflected from the spatial locations as a function of time, and (ii) determining a presence or absence of the objects in the vessel based on the number of spatial locations whose respective maximum amount of reflected light, over time, exceeds a predetermined value.

Disclosed herein are light sheet imaging systems for imaging fluorescent samples. Also disclosed herein are sample holder systems for high throughput light sheet imaging of multiple three-dimensional samples without user intervention. Further disclosed herein are automated image processing methods to identify and quantify fluorescent particles within three-dimensional image sets without user intervention or user bias.