Abstract: Disclosed herein are systems and methods for normalizing the titer of a first stain to a titer of the same stain in a template image. Also disclosed are methods of assessing stain titer levels.

Abstract: An image analysis system for analyzing biological specimen images is disclosed. The system may include: a superpixel generator configured to obtain a biological specimen image and group pixels of the biological specimen image into a plurality of superpixels; a feature extractor configured to extract, from each superpixel in the plurality of superpixels, a feature vector comprising a plurality of image features; a clustering engine configured to assign the plurality of superpixels to a predefined number of clusters, each cluster being characterized by a centroid vector of feature vectors of superpixels assigned to the cluster; and a storage interface configured to store, for each superpixel in the plurality of superpixels, clustering information identifying the one cluster to which the superpixel is assigned. The system may also include a graph engine configured construct a graph based on the stored information, and use the graph to perform a graph-based image processing task.

Abstract: This disclosure describes methods, kits, and systems for scoring the immune response to cancer through examination of tissue infiltrating lymphocytes (TILs). Methods of scoring the immune response in cancer using tissue infiltrating lymphocytes include detecting CD3, CD8, CD20, and FoxP3 within the sample and scoring the detection manually or scoring the digital images of the staining with the aid of image analysis and algorithms.

Abstract: Aspects of the present disclosure pertain to systems and methods for enhancing brightfield or darkfield images to better enable nucleus detection. In some embodiments, the systems and methods described herein are useful for identifying membrane stain biomarkers as well as nuclear/cytoplasm stain biomarkers in stained images of biological samples. In some embodiments, the presently disclosed systems and methods enable quick and accurate nucleus detection in stained images of biological samples, especially for original stained images of biological samples where the nuclei appear faint.

Abstract: The present disclosure provides systems and methods for separating colors in an image by automatically selecting color reference vectors that take into consideration the effect of light scattering, and principally how the light scattering changes the proportions of RGB channel signals in detected light at varying stain concentrations.

Abstract: This disclosure describes methods, kits, and systems for scoring the immune response to cancer through examination of tissue infiltrating lymphocytes (TILs). Methods of scoring the immune response in cancer using tissue infiltrating lymphocytes include detecting CD3, CD8, CD20, and FoxP3 within the sample and scoring the detection manually or scoring the digital images of the staining with the aid of image analysis and algorithms.

Abstract: The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.

Abstract: The present disclosure provides systems and methods for separating colors in an image by automatically selecting color reference vectors that take into consideration the effect of light scattering, and principally how the light scattering changes the proportions of RGB channel signals in detected light at varying stain concentrations.

Abstract: Disclosed herein are systems and methods for normalizing the titer of a first stain to a titer of the same stain in a template image. Also disclosed are methods of assessing stain titer levels.

Abstract: An image analysis system for analyzing biological specimen images is disclosed. The system may include: a superpixel generator configured to obtain a biological specimen image and group pixels of the biological specimen image into a plurality of superpixels; a feature extractor configured to extract, from each superpixel in the plurality of superpixels, a feature vector comprising a plurality of image features; a clustering engine configured to assign the plurality of superpixels to a predefined number of clusters, each cluster being characterized by a centroid vector of feature vectors of superpixels assigned to the cluster; and a storage interface configured to store, for each superpixel in the plurality of superpixels, clustering information identifying the one cluster to which the superpixel is assigned. The system may also include a graph engine configured construct a graph based on the stored information, and use the graph to perform a graph-based image processing task.

Abstract: The present disclosure relates, among other things, to an image analysis method for identifying objects belonging to a particular objet class in a digital image of a biological sample. The method may include, among other things, analyzing the digital image for automatically or semi-automatically identifying objects in the digital image; analyzing the digital image for identifying, for each object, a first object feature value of a first object feature of said object; analyzing the digital image for computing one or more first context feature values; inputting both the first object feature value of each of the objects in the digital image and the first context feature value of said digital image into a first classifier; and executing the first classifier.

Abstract: An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

Abstract: An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

Abstract: The present disclosure relates, among other things, to an image analysis method for identifying objects belonging to a particular objet class in a digital image of a biological sample. The method may include, among other things, analyzing the digital image for automatically or semi-automatically identifying objects in the digital image; analyzing the digital image for identifying, for each object, a first object feature value of a first object feature of said object; analyzing the digital image for computing one or more first context feature values; inputting both the first object feature value of each of the objects in the digital image and the first context feature value of said digital image into a first classifier; and executing the first classifier.

Abstract: This disclosure describes methods, kits, and systems for scoring the immune response to cancer through examination of tissue infiltrating lymphocytes (TILs). Methods of scoring the immune response in cancer using tissue infiltrating lymphocytes include detecting CD3, CD8, CD20, and FoxP3 within the sample and scoring the detection manually or scoring the digital images of the staining with the aid of image analysis and algorithms.

Abstract: Stereospecific carbonyl reductases SCR1, SCR2, and SCR3 are described herein as are nucleotide sequences that encode these reductases. These stereospecific carbonyl reductases have anti-Prelog selectivity and have specificities that are useful for fine biochemical synthesis.

Abstract: Stereospecific carbonyl reductases SCR1, SCR2, and SCR3 are described herein as are nucleotide sequences that encode these reductases. These stereospecific carbonyl reductases have anti-Prelog selectivity and have specificities that are useful for fine biochemical synthesis.

Abstract: Stereospecific carbonyl reductases SCR1, SCR2, and SCR3 are described herein as are nucleotide sequences that encode these reductases. These stereospecific carbonyl reductases have anti-Prelog selectivity and have specificities that are useful for fine biochemical synthesis.

Abstract: A system and method are provided for the measurement and display of attributes of an object of interest in a medical image. An object of interest is identified, such as a lesion or cluster of lesions in a breast area on a mammography image. At least one attribute of the lesion is then automatically measured, such as the area of the lesion, the width and height of a cluster of lesions, the number of lesions in a cluster, or the distance from one or more lesions to an anatomical feature such as the nipple, skin line or chest wall. The measurements are then displayed to a user, for example by displaying the measurements on the mammography image. Additionally, anatomical zones, such as standard, quadrant and clock zones of the breast area may be determined and displayed on the mammography image or diagram to display the location of the lesion as it corresponds to the zones.

Abstract: A system and method is provided for automatically detecting and marking a region of interest in a medical image. A region of interest is first identified in the medical image, such as a nipple of a breast region in a mammography image. The identified nipple is then marked with a digital indicator, such as a bright circle, so that a user viewing the image can easily identify the nipple. The digital indicator can be temporarily removed so that no portion of the region of interest is obscured by the digital indicator. A region of interest corresponding to another anatomical feature may also be marked, such as a pectoral muscle, which may be marked by placing a digital outline around the border of the pectoral muscle. A user viewing the medical image with the digital indicators can easily identify the marked regions of interest.