A urine analysis system according to an embodiment includes: a testing apparatus that measures particles included in a urine sample according to a flow cytometry method; an image capturing apparatus that captures images of particles in the urine sample to acquire particle images; and a management apparatus that receives a measurement result obtained by the testing apparatus and the particle images acquired by the image capturing apparatus. The management apparatus generates an order to capture an image of the urine sample based on the measurement result obtained by the testing apparatus. The image capturing apparatus executes the image capturing processing of the particles in the urine sample for which the image capturing order has been generated by the management apparatus, and transmits the acquired particle images to the management apparatus.

Automation of microscopic pathological diagnosis relies on digital image quality, which, in turn, affects the rates of false positive and negative cellular objects designated as abnormalities. Cytogenetic biodosimetry is a genotoxic assay that detects dicentric chromosomes (DCs) arising from exposure to ionizing radiation. The frequency of DCs is related to radiation dose received, so the inferred radiation dose depends on the accuracy of DC detection. To improve this accuracy, image segmentation methods are used to rank high quality cytogenetic images and eliminate suboptimal metaphase cell data in a sample based on novel quality measures. When sufficient numbers of high quality images are found, the microscope system is directed to terminate metaphase image collection for a sample. The International Atomic Energy Agency recommends at least 500 images be used to estimate radiation dose, however often many more images are collected in order to select the metaphase cells with good morphology for analysis. Improvements in DC recognition increase the accuracy of dose estimates, by reducing false positive (FP) DC detection. A set of chromosome morphology segmentation methods selectively filtered out false DCs, arising primarily from extended prometaphase chromosomes, sister chromatid separation and chromosome fragmentation. This reduced FPs by 55% and was highly specific to the abnormal structures (≥97.7%). Additional procedures were then developed to fully automate image review, resulting in 6 image-level filters that, when combined, selectively remove images with consistently unparsable or incorrectly segmented chromosome morphologies. Overall, these filters can eliminate half of the FPs detected by manual image review. Optimal image selection and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Consequently, the average dose estimation error was reduced from 0.4 Gy to <0.2 Gy with minimal manual review required. Automated image selection with these filters reduces the number of images that are required to capture metaphase cells, thus decreasing the number of images and time required for each sample. A microscope system integrates image selection procedures controls with an automated digitally controlled microscope then determines at what point a sufficient number of metaphase cell images have been acquired to accurately determine radiation dose, which then terminates data collection by the microscope. These image filtering approaches constitute a reliable and scalable solution that results in more accurate and rapid radiation dose es

A spiking retina microscope comprising microscope optics and a neuromorphic imaging sensor. The microscope optics are configured to direct a magnified image of a specimen onto the neuromorphic imaging sensor. The neuromorphic imaging sensor comprises a plurality of sensor elements that are configured to generate spike signals in response to integrated light from the magnified image reaching a threshold. The spike signals may be processed by a processor unit to generate a result, such as tracking biological particles in a specimen comprising biological material.

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.

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.

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.

An analysis and observation device includes: a component analysis section that performs component analysis of an analyte; an output section that outputs one component analysis result to an analysis history holding section; the analysis history holding section that holds a plurality of component analysis results as an analysis history; and an identifying section that identifies a component analysis result similar to the component analysis result obtained by the component analysis section from among the plurality of component analysis results held in the analysis history holding section. The analysis history holding section holds the analysis history to which the component analysis result has been newly added according to the output of the component analysis result by the output section, and the identifying section identifies a component analysis result similar to the one component analysis result from among results of the component analysis performed by the component analysis section.

The method of the present invention classifies the nuclei in prostate tissue images with a trained deep learning network and uses said nuclear classification to classify regions, such as glandular regions, according to their malignancy grade. The method according to the present disclosure also trains a deep learning network to identify the category of each nucleus in prostate tissue image data, said category representing the malignancy grade of the tissue surrounding the nuclei. The method of the present disclosure automatically segments the glands and identifies the nuclei in a prostate tissue data set. Said segmented glands are assigned a category by at least one domain expert, and said category is then used to automatically assign a category to each nucleus corresponding to the category of said nucleus' surrounding tissue. A multitude of windows, each said window surrounding a nucleus, comprises the training data for the deep learning network.

A cell manipulation panel includes a pixel array defining multiple pixels, an insulating layer forming multiple vias, and a cell gap provided with a fluid medium having cells therein. Each pixel has a TFT and corresponds to a corresponding via. The TFT includes a gate electrode, a first electrode, and a second electrode partially exposed to the fluid medium through the corresponding via. For each pixel, in an operational mode, when the gate electrode is provided with an OFF signal and the first electrode is not grounded, the TFT is turned off, allowing one of the cells in the fluid medium to be captured in the corresponding via by a dielectrophoresis (DEP) force. When the gate electrode is provided with an ON signal and the first electrode is grounded, the TFT is turned on, and the second electrode is grounded to release the captured cell to the fluid medium.

Aspects of the present disclosure relate to systems and methods for generating a composite image. This can include a western blot imager with a real time camera. One aspect of the present disclosure relates to an imaging system. The imaging system includes a sample plane that can receive and hold a sample, a photon resolving camera, and a lens attached to the photon resolving camera, the photon resolving camera and the lens positioned to image the sample plane.