Perkinelmer Cellular Technologies Germany Gmbh

Evotec Biosystems Ag

Perkinelmer Health Sciences, Inc.

Evotec Ag

Olympus Corporation

Perkinelmer Health Services, Inc.

Perkinelmer Cellular Technologies German Gmbh

Perkinelmer Cellular Technologies Germany Gmbh Eesti Filiaal

Peet Kask

Kaupo Palo

Ali Behrooz

Hartwig Preckel

Karsten Gall

Stefan Jäger

Jeffrey Meganck

Stefan Lüpke

Stefan Hummel

Olavi Ollilainen

Ülo Mets

Martin Klumpp

Presented herein are systems and methods for registering one or more images of one or more subjects based on the automated generation of artificial landmarks. An artificial landmark is a point within an image that is associated with a specific physical location of the imaged region. The artificial landmarks are generated in an automated and robust fashion along the bones of a subject's skeleton that are represented in the image (e.g. graphically). The automatically generated artificial landmarks are used to correct distortion in a single image or to correct distortion in and/or co-register multiple images of a series of images (e.g. recorded at different time points). The artificial landmark generation approach described herein thereby facilitates analysis of images used, for example, for monitoring the progression of diseases such as pulmonary diseases.

Systems and methods for automated distortion correction and/or co-registration of three-dimensional images using artificial landmarks along bones

Described herein are computationally efficient systems and methods for processing and analyzing two-dimensional (2D) and three-dimensional (3D) images using texture filters that are based on the Hessian eigenvalues (e.g., eigenvalues of a square matrix of second-order partial derivatives) of each pixel or voxel. The original image may be a single image or a set of multiple images. In certain embodiments, the filtered images are used to calculate texture feature values for objects such as cells identified in the image. Once objects are identified, the filtered images can be used to classify the objects, for image segmentation, and/or to quantify the objects (e.g., via regression).

Systems and methods for segmentation and analysis of 3D images

Presented herein, in certain embodiments, are approaches for robust bone splitting and segmentation in the context of small animal imaging, for example, microCT imaging. In certain embodiments, a method for calculating and applying single and hybrid second-derivative splitting filters to gray-scale images and binary bone masks is described. These filters can accurately identify the split lines/planes of the bones even for low-resolution data, and hence accurately morphologically disconnect the individual bones. The split bones can then be used as seeds in region growing techniques such as marker-controlled watershed segmentation. With this approach, the bones can be segmented with much higher robustness and accuracy compared to prior art methods.

System and methods for automated segmentation of individual skeletal bones in 3D anatomical images

Systems and methods for automated segmentation of individual skeletal bones in 3D anatomical images

An apparatus for confocal observation of a specimen includes an illumination device. The illumination device generates illumination radiations of at least two different wavelengths. With the aid of a mask device illuminated by the illumination radiations, one mask image is generated per wavelength. An objective serves for imaging said mask images in the specimen. With the aid of a beam splitter device, the emission radiations emitted by the specimen are divided based on the wavelengths, and are detected based on the wavelengths by a detection device.

Apparatus for confocal observation of a specimen

A new family of morphological features, referred to herein as threshold compactness features, is provided, useful for automated classification of objects, such as cells, in images. In one embodiment, one or more thresholds and/or binary masks are applied to an image, and one or more provisional objects within a cell in the image are automatically identified. The threshold compactness of the cell is computed as a function of area S of the one or more provisional objects and border length P of the one or more provisional objects. Computation of threshold compactness allows cells in an image to be distinguished and characterized. Compared to previous techniques, the methods and apparatus described herein are more robust and computationally efficient.

Methods and apparatus for image analysis using threshold compactness features

Described herein are systems and methods for efficient and accurate, automated detection of a region of interest interior to the ribcage from an in vivo mammalian image. It is found that efficient, automated identification of the region of interest interior to the ribcage can be achieved by the use of both a bone distance mask and a surface distance mask. The technique solves the problem of accurate and fast identification of the region of interest for a wide range of sizes and shapes of mammals, e.g., small mammals such as mice.

Method and system for automated detection of tissue interior to a mammalian ribcage from an in vivo image

In various embodiments, methods and apparatus are provided for automated selection of features of cells useful for classifying cell phenotype. The methods include determining a signal-to-noise ratio (S/N) for each of a plurality of pairs of features, rather than S/N for individual features. The approach is capable of quickly identifying a small set of features of imaged cells that are most relevant for classification of a desired cell phenotype from among a very large number of features. The small group of relevant features can then be used to more efficiently and more accurately classify phenotype of unidentified cells.

Methods and apparatus for fast identification of relevant features for classification or regression

A new morphological feature referred to herein as profile weighted intensity feature is provided, useful for automated classification of objects, such as cells, that are depicted in digital images. In certain embodiments, the profile weighted intensity feature is determined by automatically identifying a border of a cell in an input image, determining a distance image for the cell, computing a profile function for the cell, and computing a mean intensity of at least a portion of the input image weighted by the profile function for the cell.

Methods and apparatus for image analysis using profile weighted intensity features

A method for characterizing samples having units, by monitoring fluctuating intensities of radiation emitted, scattered and/or reflected by said units in at least one measurement volume, the monitoring being performed by at least one detection means, said method comprising the steps of:

Method of analysis of samples by determination of a function of specific brightness

A method for distinguishing samples having flourescent particles includes (a) monitoring intensity fluctuations of fluorescence emitted by the particles in at least one measurement volume by detecting sequences of photon counts by at least one photon detector, (b) determining, from the sequences of photon counts, intermediate statistical data involving at least two probability functions of the number of photon counts detected in different sets of counting time intervals, and (c) determining from the intermediate statistical data a distribution of particles as a function of at least two arguments, wherein one argument is a specific brightness of the particles, or a measure thereof, and another argument is a diffusion coefficient of the particles, or a measure thereof.

Fluorescence intensity multiple distributions analysis: concurrent determination of diffusion times and molecular brightness

Fluorescence intensity and lifetime distribution analysis

A method for characterizing a sample involves: a) monitoring the sample for intensity fluctuations of radiation emitted, scattered and/or reflected by units of the sample in at least one measurement volume with at least one detector which is able to detect radiation emitted, scattered and/or reflected by the units, b) determining from the intensity fluctuations intermediate statistical data of an at least two-dimensional joint statistical function, and c) determining information related to a joint distribution of the units out of the intermediate statistical data.

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