Abstract: Systems and techniques to acquire image data. In general, in one implementation, the technique includes: acquiring image data for a plurality of areas of a scan region. The technique may further include processing the image data to determine whether an area includes one or more desired features. The technique may further include designating an area as “interesting” if it includes a desired feature, and designating an area as non-interesting if it does not include the desired feature. The areas may be sub-divided one or more times, and subdivisions may be analyzed to determine if they include a desired feature.

Abstract: The present disclosure includes systems and techniques relating to intelligently directed segmentation analysis for automated microscope systems. In general, in one implementation, the technique includes obtaining an image of at least a portion of a scan region including a biological specimen, partitioning the obtained image into zelles, determining one or more parameters of the zelles, performing a cluster analysis on the one or more parameters of the zelles, differentiating tissue of greater interest from tissue of lesser interest in the obtained image based on the cluster analysis and based on a test being performed for the biological specimen, and storing more information for the tissue of greater interest than information for the tissue of lesser interest. The cluster analysis can be a multivariate statistical cluster analysis, and the zelles can be test-dependent zelles (e.g., having dimensions defined according to the test being performed for the biological specimen).

Abstract: Automated projection of serial section images in microscopic imaging systems. A computer-controlled microscope that is connected electrically to a controller that has a display device. A batch of serial section slides includes a series of serial section slides, each of which has a microscope slide, a sample, and an interesting area. If one of the images is misregistered, then the images may be transformed.

Abstract: A method including acquiring images of medical slides at a plurality of different focus positions, determining a position which produces a maximum value of pixel values relative to a pixel value mean, wherein said determining comprises using a pixel value mean as a coarse estimate of coarse focus position, and subsequently refining said coarse focus position to find a fine focus position, and wherein said refining comprises fitting to a polynomial, and using a specified portion of the polynomial as a fine estimate of focus position, and producing a focus control signal that is related to said maximum value to control a focus position.

Abstract: Systems and techniques to acquire image data. In general, in one implementation, the technique includes: acquiring image data for a plurality of areas of a scan region. The technique may further include processing the image data to determine whether an area includes one or more desired features. The technique may further include designating an area as “interesting” if it includes a desired feature, and designating an area as non-interesting if it does not include the desired feature. The areas may be sub-divided one or more times, and subdivisions may be analyzed to determine if they include a desired feature.

Abstract: The present disclosure includes systems and techniques relating to intelligently directed segmentation analysis for automated microscope systems. In general, in one implementation, the technique includes obtaining an image of at least a portion of a scan region including a biological specimen, partitioning the obtained image into zelles, determining one or more parameters of the zelles, performing a cluster analysis on the one or more parameters of the zelles, differentiating tissue of greater interest from tissue of lesser interest in the obtained image based on the cluster analysis and based on a test being performed for the biological specimen, and storing more information for the tissue of greater interest than information for the tissue of lesser interest. The cluster analysis can be a multivariate statistical cluster analysis, and the zelles can be test-dependent zelles (e.g., having dimensions defined according to the test being performed for the biological specimen).

Abstract: Systems and techniques for reducing repositioning inaccuracies in imaging systems. Initial image data and positional information is received and cross-correlated with additional image data and positional information. An offset may be determined based on the cross-correlation.

Abstract: A method including acquiring images of medical slides at a plurality of different focus positions, determining a position which produces a maximum value of pixel values relative to a pixel value mean, wherein said determining comprises using a pixel value mean as a coarse estimate of coarse focus position, and subsequently refining said coarse focus position to find a fine focus position, and wherein said refining comprises fitting to a polynomial, and using a specified portion of the polynomial as a fine estimate of focus position, and producing a focus control signal that is related to said maximum value to control a focus position.

Abstract: A method and apparatus for automated cell analysis of biological specimens automatically scans at a low magnification to acquire images which are analyzed to determine candidate cell objects of interest. The low magnification images are converted from a first color space to a second color space. The color space converted image is then filtered. The candidate interest pixels are morphologically processed and compared to blob parameters to identify candidate objects of interest which correspond to cells or other structures relevant to medical diagnosis of the biological specimen. The location coordinates of the objects of interest are stored and additional images of the candidate cell objects are acquired at high magnification.

Abstract: Systems and techniques to acquire image data. In general, in one implementation, the technique includes: acquiring image data for a plurality of areas of a scan region. The technique may further include processing the image data to determine whether an area includes one or more desired features. The technique may further include designating an area as “interesting” if it includes a desired feature, and designating an area as non-interesting if it does not include the desired feature. The areas may be sub-divided one or more times, and subdivisions may be analyzed to determine if they include a desired feature.

Abstract: Provided are methods, compositions and kits for efficiently and accurately identifying rare events in a biological sample.