Abstract: Methods, systems, workstations, and computer programs with user interactive patch gallery user interfaces that allow a user to alter at least one feature dimension of the patches and/or WSI image using the patch gallery. The patches are different small regions of interest of excerpts of the WSI and are not required to be neighboring excerpts but can be random samples spanning an extent of the feature dimension.

Abstract: A method of image classification is used for classifying images of stained cell nuclei. For each nucleus, the total integrated optical density is calculated and a histogram calculated. The image is then classified by automatically identifying peaks, identifying the lowest peak as a 2C peak and classifying the image as at least one of diploid, tetraploid, aneuploid or polyploid based on the number of peaks and the count at an integrated optical densities above the 2C peak.

Abstract: A system is capable of detecting substrates and can differentiate between zero, one, or multiple transparent or semi-transparent substrates in a stack. The system can include an optical sensor, an optically anti-reflective element, and a detector. The optical sensor outputs light towards the optically anti-reflective element. The light detector is positioned to detect light from the light source that is reflected by substrates, if any, positioned within a detection zone between the optically anti-reflective element and the detector.

Abstract: Methods and devices for cytometric analysis are provided. A cytometry apparatus is provided which may be used with a stationary sample cuvette for analysis of a stationary sample or with a flow sample cuvette for analysis of a flowing sample. The methods and devices provided herein may be used to perform cytometric analysis of samples under a wide range of experimental and environmental conditions.

Abstract: The present disclosure relates to a phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving, by a computing device, a plurality of candidate images of candidate biological cells each having a respective candidate phenotype. The method also includes obtaining, by the computing device for each of the plurality of candidate images, a semantic embedding associated with the respective candidate image. Further, the method includes grouping, by the computing device, the plurality of candidate images into a plurality of phenotypic strata based on their respective semantic embeddings.

Abstract: In an inspection apparatus, a target on the surface is illuminated with illuminating radiation that comprises first and second illuminating components. The illuminating components form one or more periodic illuminating patterns on the surface. A plurality of scattered radiation patterns formed by the illuminating radiation after scattering by the target is captured at a detector for a number of values of a controllable characteristic of at least one of the illuminating components. The captured radiation is then used to reconstruct data describing the target.

Abstract: A skin condition detection method and an electronic device are provided. The method includes: acquiring a first image; performing at least one skin condition detection on the first image to acquire at least one first characteristic value; acquiring at least one second characteristic value acquired through the skin condition detection in the second image; determining whether a skin condition in the first image has changed with respect to a skin condition in the second image according to the first characteristic value and the second characteristic value; and if the skin condition in the first image has changed with respect to the skin condition in the second image, outputting notification information to indicate that the skin condition in the first image has changed with respect to the skin condition in the second image. The invention makes it possible to effectively and clearly determine change of the skin condition.

Abstract: Methods, systems, and apparatuses for automatically identifying glandular regions and tubule regions in a breast tissue sample are provided. An image of breast tissue is analyzed to detect nuclei and lumen candidates, identify tumor nuclei and true lumen from the candidates, and group tumor nuclei with neighboring tumor nuclei and lumina to define tubule glandular regions and non-tubule glandular regions of the image. Learnt supervised classifiers, such as random forest classifiers, can be applied to identify and classify the tumor nuclei and true lumina. Graph-cut methods can be applied to group the tumor nuclei and lumina and to define the tubule glandular regions and non-tubule glandular regions. The analysis can be applied to whole slide images and can resolve tubule areas with multiple layers of nuclei.

Abstract: A lens scanning mode hyperspectral imaging system and a rotor unmanned aerial vehicle include: an imaging lens, an imaging spectrometer and a surface array detector arranged in sequence and coaxial to a main optic axis, wherein the imaging spectrometer and the surface array detector are connected and mounted to each other; wherein the lens scanning mode hyperspectral imaging system further includes: a driving device for driving the imaging lens to translate relative to a plane where a slit of the imaging spectrometer is. The hyperspectral imaging system of the present invention overcomes the technical bias in the prior art that the imaging lens must be fixed, and the present invention provides relative motion between the target object and the imaging spectrometer by the lens scanning mode for imaging, which solves the image distortion problem of conventional hyperspectral imaging system using a slit scanning mode or a scanning mode.

Abstract: An image-acquisition system includes a microscope apparatus that acquires an image of a specimen; a map-image-acquisition portion that controls the microscope apparatus so as to acquire, at a low magnification, a map image including a plurality of anatomical regions in the specimen; an interface portion that allows a user to specify a desired anatomical region as a target region by means of unique IDs assigned to the individual anatomical regions; a processing portion that calculates a spatial position of the target region on the basis of the map image and atlas data having positional information of the individual anatomical regions; and a high-resolution image-acquisition portion that controls, on the basis of the spatial position of the target region, the microscope apparatus so as to acquire a high-resolution image of the target region in the specimen at a magnification that is greater than that of the map image.

Abstract: This lensless imaging system comprises a receiving support configured to receive a sample, a light source configured to emit a light beam illuminating the sample in an illumination direction, the light source including a diode and a diaphragm, the diaphragm being positioned between the diode and the receiving support in the lighting direction, and a matrix photodetector configured to acquire at least one image of the sample, each image being formed by radiation emitted by the illuminated sample and including at least one elementary diffraction pattern, the receiving support being positioned between the light source and the matrix photodetector in the illumination direction. The system further comprises a light diffuser positioned between the diode and the diaphragm.

Abstract: A method of classifying signals using non-linear sparse representations includes learning a plurality of non-linear dictionaries based on a plurality of training signals, each respective nonlinear dictionary corresponding to one of a plurality of class labels. A non-linear sparse coding process is performed on a test signal for each of the plurality of non-linear dictionaries, thereby associating each of the plurality of non-linear dictionaries with a distinct sparse coding of the test signal. For each respective non-linear dictionary included in the plurality of non-linear dictionaries, a reconstruction error is measured using the test signal and the distinct sparse coding corresponding to the respective non-linear dictionary. A particular nonlinear dictionary corresponding to a smallest value for the reconstruction error among the plurality of non-linear dictionaries is identified and a class label corresponding to the particular non-linear dictionary is assigned to the test signal.

Abstract: A large scale cell image analysis method and system are provided. The method includes: obtaining a cell image; performing a region segmentation process; and performing a feature calculation process. The region segmentation process includes: performing a statistical intensity algorithm according to the cell image to calculate a first threshold and a second threshold; dividing the cell image into a background region and a cell region according to the first threshold; performing an average intensity process according to the cell region to calculate a third threshold and a fourth threshold; and dividing the cell region into a cytoplasmic region and a nucleus region according to the third threshold and the fourth threshold. The feature calculation process calculates at least one feature at least according to the cell region, the nucleus region, and the cytoplasmic region.

Abstract: An image-feature-value calculating unit extracts an image feature value of an image of a cell candidate area. An NRBC discriminating unit uses a pre-trained discriminator to identify whether or not a target cell is shown in the cell candidate area, on the basis of the image feature value of the image of the cell candidate area. When the cell candidate area is identified as an area in which a target cell is shown, a discrimination result display unit displays the image of the cell candidate area. When the cell candidate area is identified as an area in which a target cell is shown, a discriminator training unit trains the discriminator by using the image feature value of the image of the cell candidate area as a training sample on the basis of a user input about whether or not a target cell is shown in the cell candidate area.

Abstract: An image processing method and an image processing device are provided. The image processing method includes steps of extracting a feature of an inputted first image by a first CNN, and reconstructing and outputting an image by a second CNN. The first CNN includes a plurality of first convolutional layers connected sequentially to each other and a plurality of first pooling layers each arranged between respective adjacent first convolutional layers, and each first convolutional layer is configured to generate and output a first convolutional feature. The second CNN includes a plurality of second convolutional layers connected sequentially to each other and a plurality of composite layers each arranged between respective adjacent second convolutional layers, and each composite layer is an up-sampling layer.

Abstract: Techniques for segmentation of organs and tumors and cells in image data include revising a position of a boundary by evaluating an evolution equation that includes differences of amplitude values for voxels on the boundary from a statistical metric of amplitude of voxels inside, and from a statistical metric of amplitude of voxels outside, for a limited region that lies within a distance r of the boundary. The distance r is small compared to a perimeter of the first boundary. Some techniques include determining a revised position of multiple boundaries by evaluating an evolution equation that includes differences in a first topographical distance from a first marker and a second topographical distance from a second marker for each voxel on the boundary, and also includes at least one other term related to boundary detection.

Abstract: A cell analysis apparatus including: a cell image acquisition unit that acquires an image of cells in a culture vessel in which cells are cultured; a smoothing processing unit that smooths luminance values in the image acquired by the cell image acquisition unit; a minimum-value detecting unit that detects minimum values of the luminance values smoothed by the smoothing processing unit; a smallest-minimum-value extracting unit that extracts smallest minimum values which are the smallest in regions according to the sizes of the cells, from the minimum values detected by the minimum-value detecting unit; a counting unit that counts the number of smallest minimum values extracted by the smallest-minimum-value extracting unit; and a cell-count calculating unit that calculates the number of cells in the culture vessel on the basis of the number of smallest minimum values counted by the counting unit.

Abstract: A system predicts the recurrence of cancer. A first slice of a prostate tissue sample is stained so that luminal epithelial cells and basal epithelial cells are stained different colors. A first digital image is taken of the first slice. The second slice of the sample is stained so that M1 type macrophages and M2 type macrophages are differentially stained. A second digital image is taken of the second slice. The system analyzes the first digital image and defines regions of non-intact glands. Intact gland regions are then determined, and regions of stroma are identified. The system defines influence zones between non-intact regions and stroma regions. Using information from the second image, macrophages in the tissue corresponding to the influence zones are identified and counted. Based at least in part on this count, the system determines a score. The score is indicative of whether the patient will experience PSA recurrence.

Abstract: Provided herein is a method and system for analyzing a sample. In some embodiments the method makes use of a plurality of capture agents that are each linked to a different oligonucleotide and a corresponding plurality of labeled nucleic acid probes, wherein each of the labeled nucleic acid probes specifically hybridizes with only one of the oligonucleotides. The sample is labeled with the capture agents en masse, and sub-sets of the capture agents are detected using iterative cycles using corresponding subsets of the labeled nucleic acid probes.

Abstract: A method for training a segmentation correction model includes performing an iterative model training process over a plurality of iterations. During each iteration, an initial segmentation estimate for an image is provided to a human annotators via an annotation interface. The initial segmentation estimate identifies one or more anatomical areas of interest within the image. Interactions with the annotation interface are automatically monitored to record annotation information comprising one or more of (i) segmentation corrections made to the initial segmentation estimate by the annotators via the annotation interface, and (ii) interactions with the annotation interface performed by the annotators while making the corrections. A base segmentation machine learning model is trained to automatically create a base segmentation based on the image. Additionally, a segmentation correction machine learning model is trained to automatically perform the segmentation corrections based on the image.

Abstract: Methods, apparatus, and other embodiments detect nuclei in histopathological images. One example method includes accessing a histopathology image that includes a plurality of pixels, generating a gradient field map based on the histopathology image, generating a refined gradient field map based on the gradient field map, calculating a voting map for a member of the plurality of pixels based, at least in part, on the refined gradient field map and a voting kernel, generating an aggregated voting map based on the voting map, computing a global threshold, and identifying a nuclear centroid based on the global threshold and the aggregated voting map.

Abstract: The present invention relates to a cell assessment method characterized in including an acquisition step of acquiring an optical path length image of a small cell clump, an extraction step of extracting a cell nucleus region within the acquired optical path length image, a comparison step of comparing an optical path length of an inside and an optical path length of an outside of the extracted cell nucleus region, and an assessment step of assessing whether or not a cell is a stem cell based on the comparison results.

Abstract: This invention pertains to a method for microscopically imaging a sample, with a digital scanner comprising a sensor including a 2D array of pixels and to a digital scanning microscope carrying out this method. It is notably provided a method for microscopically imaging a sample with a scanner comprising a sensor including a 2D array of pixels in an XY coordinate system, the axis Y being substantially perpendicular to the scan direction, wherein the scanner is arranged such that the sensor can image an oblique cross section of the sample, and wherein the method comprises the steps of: • activating a first sub-array of the 2D array of pixels, the first sub-array extending mainly along the Y axis at a first X coordinate (X1), • creating a first image by imaging a first area of the sample by means of the first sub-array of pixels. According to aspects of the invention, it is further proposed a scanner carryout this method and using the same 2D array sensor for imaging and auto-focusing purpose.

Abstract: Systems, devices, and methods for printing on surfaces of three-dimensional objects are provided. The systems, devices, and methods allow for images, and three-dimensional structures, to be printed onto a surface of a three-dimensional object. The surface of the three-dimensional object can have many different shapes, including an arbitrary or non-uniform shape having multiple curves. In one exemplary embodiment, the method includes associating a pattern of polygons with a surface of a three-dimensional object and then scaling a pattern of polygons associated with an image to be printed onto the surface with the pattern of polygons associated with the surface. One or more polygons of the scaled pattern of polygons are then progressively projected onto the surface, and a photosensitive material associated with the surface is cured to set projected image portion on the surface. Systems, devices, and other methods for printing onto surfaces of three-dimensional objects are also provided.

Abstract: A method of manufacturing a tube, and a tube, the method comprising the steps of: coupling in position a first tube body and a second tube body by inserting a first insert portion of a connecting pin into an end portion of a first hole of the first tube body and inserting a second insert portion into an end portion of a first hole of the second tube body; welding all around an outer circumferential portion of the coupled portion of the first tube body and the second tube body and all around an inner circumferential portion of the first holes in which the connecting pin is inserted; and removing the connecting pin remaining inside the first hole of the first tube body and the first hole of the second tube body.

Abstract: An information processing apparatus includes a circuitry configured to provide a slide image including a sample image and a label image which are obtained by shooting a slide, generate a first image that a specific area of the slide image is concealed, output a thumbnail image, wherein the thumbnail image is one of the slide image and the first image.

Abstract: A digital microscope apparatus includes: an observation image capturing unit configured to capture an observation image of each of a plurality of small areas, an area containing a sample on a glass slide being partitioned by the plurality of small areas; and a controller configured to set at least one evaluation area for the observation image of each of the plurality of small areas, the observation image being captured by the observation image capturing unit, to perform an edge detection on the at least one evaluation area, and to calculate, using results of the edge detection on two evaluation areas that are closest between two of the observation images adjacently located in a connected image, a difference in blur evaluation amount between the two observation images, the connected image being obtained by connecting the observation images according to the partition.

Abstract: Systems and methods that receive as input microscopy images, extract features, and apply layers of processing units to compute one or more set of cellular phenotype features, corresponding to cellular densities and/or fluorescence measured under different conditions. The system is a neural network architecture having a convolutional neural network followed by a multiple instance learning (MIL) pooling layer. The system does not necessarily require any segmentation steps or per cell labels as the convolutional neural network can be trained and tested directly on raw microscopy images in real-time. The system computes class specific feature maps for every phenotype variable using a fully convolutional neural network and uses multiple instance learning to aggregate across these class specific feature maps. The system produces predictions for one or more reference cellular phenotype variables based on microscopy images with populations of cells.

Abstract: A system and method for automatically analyzing an image of cells to detect cancer. Some embodiments include eliciting and receiving a digital photomicrograph image of cells; determining a boundary of a cell in the image; identifying a plurality of characteristics of the cell from image-pixel data from within the identified boundary of the cell; reading a plurality of cell characteristics of a plurality of types of cells from a database; comparing the identified characteristics of the cells in the image to the plurality of cell characteristics read from the database; and determining a pathology based on the comparing. Some embodiments further include automatically identifying an appropriate treatment and applying the identified treatment.

Abstract: A device for extracting at least one object characteristic of an object (106) is presented, the device comprising: a light sensor (101) for recording a hologram of an object and a processing unit (102) coupled to the light sensor and configured for extracting at least one object characteristic from the hologram; wherein the processing unit is configured for extracting the at least one object characteristic from a section of the hologram without reconstructing an image representation of the object. Further, a device (200) for sorting an object (106), a method for identifying an object and a method for sorting objects is presented.

Abstract: An artificial neural network system implemented on a computer for cell segmentation and classification of biological images. It includes a deep convolutional neural network as a feature extraction network, a first branch network connected to the feature extraction network to perform cell segmentation, and a second branch network connected to the feature extraction network to perform cell classification using the cell segmentation map generated by the first branch network. The feature extraction network is a modified VGG network where each convolutional layer uses multiple kernels of different sizes. The second branch network takes feature maps from two levels of the feature extraction network, and has multiple fully connected layers to independently process multiple cropped patches of the feature maps, the cropped patches being located at a centered and multiple shifted positions relative to the cell being classified; a voting method is used to determine the final cell classification.

Abstract: An imaging method including: controlling luminance distribution of light such that the light continuously attenuates from an inner side toward an edge in a peripheral area within an imaging range of an imaging unit; emitting, in the imaging range, the light whose luminance distribution has been controlled; imaging an object within the imaging range where the light has been emitted to generate image data; sequentially moving the imaging range relative to the object such that peripheral areas where the light attenuates overlap with each other in adjacent imaging ranges; sequentially obtaining a plurality of images with different imaging ranges based on the image data; and creating a combined image by stitching the plurality of images in such a way that adjacent images of the plurality of images corresponding to the adjacent imaging ranges are stitched together such that image areas corresponding to the peripheral areas overlap with each other.

Abstract: A method of forming an imaging calibration device for a biological material imaging system, the method comprises: providing one or more discrete regions upon or within a retaining member, each region for the receipt of a selected predetermined biological stain material; selecting one or more predetermined biological stain materials, wherein each of the selected predetermined biological stain materials has a predetermined optical response, and providing one or more of the selected predetermined biological stain materials to the said one or more discrete regions such that the said material is localised in the said region.

Abstract: The present disclosures relates to a method of detecting, classifying, and counting dots in an image of a tissue specimen comprising detecting dots in an image of the tissue sample that meet criteria for absorbance strength, black unmixed image channel strength, red unmixed image channel strength, and a difference of Gaussian threshold, wherein the detected dots correspond to in situ hybridization signals in the tissue samples; classifying the detected dots as belonging to a black in situ hybridization signal or to a red in situ hybridization signal; and calculating a ratio of those dots belonging to the black in situ hybridization signal and those belonging to the red in situ hybridization signal.

Abstract: A lenslet based integral field spectrograph (IFS) may have a design that makes better use of the detector pixels by placing adjacent spectra next to each other rather than staggering the spectra. Such a design maintains the main compactness and simplicity of prior lenslet array based IFSs, but improves the detector efficiency, which is rather low in conventional lenslet array based IFSs.

Abstract: The present invention discloses a method for finding a cell nucleus of a target cell from a cell image, wherein the cell image includes the target cell and at least one variation cell, and the target cell includes cytoplasm and the cell nucleus. The method includes steps of: (a) processing the cell image via an image processor such that the cytoplasm, the cell nucleus and the variation cell have different shades of color; (b) demarcating the outlines of the cytoplasm, the cell nucleus and the variation cell; (c) calculating geometrical reference points of the outlines; (d) calculating the distances from the geometrical reference point of the cytoplasm outline to the geometrical reference point of the cell nucleus outline and to the geometrical reference points of the variation cell outlines; and (e) finding a specific geometrical reference point having a shortest distance to locate a specific outline corresponding to the specific geometrical reference point as the cell nucleus.

Abstract: A method is proposed for identifying an anatomical structure within a spatial-temporal image (i.e. a series of frames captured as respective times). A current frame of spatial-temporal medical image is processed using information from one or more previous and/or subsequent temporal frames, to aid in the segmentation of an object or a region of interest (ROI) in a current frame. The invention is applicable to both two- and three-dimensional spatial-temporal images (i.e., 2D+time or 3D+time), and in particular to cardiac magnetic resonance (CMR images). An initialization process for this method segments the left ventricle (LV) in a CMR image by a fuzzy c-means (FCM) clustering algorithm which employs a circular shape function as part of the definition of the dissimilarity measure.

Abstract: There is provided a canceration information providing method which can detect the possibility of cancer in the initial stage with high accuracy. The canceration information providing method for providing information pertaining to canceration of cells includes: acquiring measurement data including first data pertaining to size of a cell nucleus and second data pertaining to size of a cytoplasm for each cell contained in a measurement sample which includes cells collected from epithelial tissue; extracting the measurement data of cells to be analyzed, which are at least some of the cells located toward the basal membrane side of the cells existing in the surface layer in the epithelial tissue, from the measurement data of a plurality of cells in the measurement sample based on the first data and the second data acquired for each cell; and analyzing the extracted measurement data and outputting the information pertaining to the canceration of cells.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.

Abstract: Systems and methods for capturing a digital image of a slide using an imaging line sensor and a focusing line sensor. In an embodiment, a beam-splitter is optically coupled to an objective lens and configured to receive one or more images of a portion of a sample through the objective lens. The beam-splitter simultaneously provides a first portion of the one or more images to the focusing sensor and a second portion of the one or more images to the imaging sensor. A processor controls the stage and/or objective lens such that each portion of the one or more images is received by the focusing sensor prior to it being received by the imaging sensor. In this manner, a focus of the objective lens can be controlled using data received from the focusing sensor prior to capturing an image of a portion of the sample using the imaging sensor.

Abstract: A nucleolus detection unit, which detects nucleoli in a plurality of cells in a cell image obtained by imaging the cells, and a cell recognition unit, which acquires information indicating a distance between the nucleoli and recognizes the individual cells based on the information indicating the distance, are provided.

Abstract: The identification apparatus includes a quantitative phase image acquisition unit, a feature quantity extraction unit, a learning unit, a storage unit, and an identification unit. The feature quantity extraction unit extracts a feature quantity of a quantitative phase image of a cell. The learning unit performs machine learning for a quantitative phase image of a known cell of which a type is known based on the feature quantity extracted by the extraction unit. The storage unit stores a result of the machine learning by the learning unit. The identification unit determines, based on the feature quantity extracted by the extraction unit for the quantitative phase image of an unknown cell of which a type is unknown, the type of the unknown cell using the learning result stored by the storage unit.

Abstract: A processor receives a first list comprising a plurality of events from a portion of digital data of an unknown work and one or more metrics between each pair of adjacent events from the plurality of events. The processor compares the first list to a second list comprising events and metrics between events for a known work to determine a first quantity of hits and a second quantity of misses. The processor determines whether the first list matches the second list based on the first quantity of hits and the second quantity of misses. The processor determines that the unknown work is a copy of the known work responsive to determining that the first list matches the second list.

Abstract: Certain aspects of an apparatus and method for automatic ER/PR scoring of tissue samples may include for determining a cancer diagnosis score comprising identifying a positive stained nucleus in a slide image of the tissue sample, identifying a negative stained nucleus in the slide image, computing a proportion score based on number of the positive stained nucleus identified and number of the negative stained nucleus identified and determining the cancer diagnosis score based on the proportion.

Abstract: An cell contour formation apparatus includes a cell image acquiring unit, a subband image creating unit, a features calculating unit, a correcting unit, a contour forming unit. The subband image creating unit creates subband images including a low frequency image and a high frequency image. The features calculating unit calculates a local texture features from the high frequency image. The correcting unit corrects the high frequency image on the basis of the pixel value of the low frequency image and the texture features. The contour forming unit forms contours of cells included in the cell group on the basis of the corrected high frequency image.

Abstract: Provided is a microscope system including a motor-driven stage on which is mounted a culture vessel containing one or more cell clusters, each including cells having a target molecule labeled with a fluorescent or luminescent chemical; a low-magnification-image acquiring unit that acquires low-magnification images of the cell clusters in the culture vessel mounted on the stage; a detecting unit that detects the position of each cell cluster in the culture vessel by analyzing the acquired low-magnification images; and a high-magnification-image acquiring unit that, after the detected position is aligned with the optical axis of an objective lens, acquires slice images of fluorescence or luminescence emitted from the cells forming the cell cluster at a higher magnification than the low-magnification-image acquiring unit at intervals along the optical axis while the stage and/or the lens is moved to change stepwise the distance between the lens and the cell cluster.

Abstract: An image analysis device includes a first calculating part that calculates a magnitude of motion of an image element from time-series images of pulsating myocardial cells, a second calculating part that calculates a feature quantity on the basis of the magnitude of the motion of the image element calculated by the first calculating part, and an identifying part that identifies whether the image element is a predetermined image element on the basis of the feature quantity calculated by the second calculating part.

Abstract: [Object] To provide a cell analysis system, a cell analysis program and a cell analysis method suitable for analyzing the movement of ions or molecules across cell membranes. [Solving Means] A cell analysis system according to the present disclosure includes a motion information extracting unit and a motion characteristics calculating unit. The motion information extracting unit extracts motion information arising from a movement of ions or molecules across a cell membrane, out of a cell image obtained from imaging a cell in time series. The motion characteristics calculating unit calculates motion characteristics of the motion information.

Abstract: A sample analyzer prepares a measurement sample from a blood sample or a body fluid sample which differs from the blood sample; measures the prepared measurement sample; obtains characteristic information representing characteristics of the components in the measurement sample; sets either a blood measurement mode for measuring the blood sample, or a body fluid measurement mode for measuring the body fluid sample as an operating mode; and measures the measurement sample prepared from the blood sample by executing operations in the blood measurement mode when the blood measurement mode has been set, and measuring the measurement sample prepared from the body fluid sample by executing operations in the body fluid measurement mode that differs from the operations in the blood measurement mode when the body fluid measurement mode has been set, is disclosed. A computer program product is also disclosed.

Abstract: A method for calibrating a biological instrument is provided. The method comprises the steps of acquiring an image of at least one biological sample array, determining a first region of interest within the image, wherein the first region of interest comprises a first plurality of locations on the at least one biological array; and identifying within the first region of interest, a plurality of image elements associated with each of the first plurality of locations on the at least one biological array.