Carl Zeiss Microimaging Gmbh

Cynthia B Perz

Jose De La Torre-Bueno

Robert T Ellis

Gina Marie McLaren

Mary Jean Smith

Disclosed is an imaging system that stores and retrieves very large scanned digital images, as in applications that automatically scan biological samples using a computer-controlled digital camera microscope. Slide data is entered, zelle data is captured and compressed and a zelle database file is loaded. Furthermore, an image database file is loaded and it is determined whether another zelle is to be stored. The image can be retrieved by accessing the zelle database, reading a zelle record, reading an image file, loading the display buffer, and determining whether another zelle is to be loaded.

Data structure of an image storage and retrieval system

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.

Silhouette image acquisition

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).

System for and method of intelligently directed segmentation analysis for automated microscope systems

The present disclosure includes systems and techniques relating to focusing in automated microscope systems. In general, in one implementation, the technique includes obtaining an image of at least a portion of a scan region, analyzing the image to find an area in the image representing a sample, determining a nature of the sample at a selected focus point location in the area in the image, selecting an automated focusing process for use at the selected focus point location based on the determined nature of the sample at the selected focus point location, and focusing the selected automated focusing process. The selecting can include selecting different automated focusing processes for different focus point locations based on different tissue characteristics at the locations.

System for and method of focusing in automated microscope systems

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.

Serial section analysis for computer-controlled microscopic imaging

Disclosed is a system for and a method of registering images captured by a computer-controlled microscope. The imaging system of the present invention includes a computer-controlled microscope electrically connected to a controller having a display device. The microscope further includes a barcode reader, a camera, a serial interface, one or more sensors, one or more motors, a light source, a turret, and a data interface. The method of image registration includes the steps of defining areas of interest on a microscope slide, creating a database of microscopic image locations, commanding a microscope to each location, capturing and storing an image, calculating correlation with neighbors, registering correlation scores and offsets, determining an anchor, and attaching neighbors using correlation scores and offsets.

System and method of registering field of view

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.

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Huntington Beach, CA, United States of America

Cynthia B Perz