Ventana Medical Systems, Inc.

XiuZhong Wang

Srinivas Chukka

Christophe Chefd'hotel

Kien Nguyen

Christoph Guetter

Christophe CHEFD´HOTEL

A system and a method for unveiling poorly visible or lightly colored nuclei in an input image are disclosed. An input image is fed to a color deconvolution module for deconvolution into two color channels that are processed separately before being combined. The input image is deconvolved into two separate images: a stain image and a counter stain image. A complement of the stain image is generated in order to clearly reflect the locations of the poorly visible or light-colored nuclei. The complement image and the counter stain image are optionally normalized and then combined and segmented, to generate an output image with clearly defined nuclei. Alternatively, the complement of the stain image and the counter stain image can optionally be normalized, and then segmented prior to being combined to generate the output image.

Automated methods and systems for detecting cells in stained specimen images

Systems and methods of improving segmentation and classification in multi-channel images of biological specimens are described herein. Image channels may be arranged and processed sequentially, with the first image channel being processed from an unmodified image, and subsequent images processed from attenuated images that remove features of previous segmentations. For each segmented image channel in sequence, a binary image mask of the image channel may be created. A distance transform image may be computed from the binary mask. Attenuation images computed for all previous channels may be combined with a current attenuation image to create a combined attenuation image. The next image channel in the sequence may then be attenuated to produce an attenuated next image channel, which may then be segmented. The steps can be repeated until all image channels have been segmented.

Performing segmentation of cells and nuclei in multi-channel images

Systems and methods for generating a locally adaptive threshold image for foreground detection performing operations including creating a saliency edge strength image or layer indicating edge or border pixels of the nuclei by performing tensor voting on pixels neighboring the initial edge pixels within an image region to refine true edges are featured. Further, for each of a plurality of regions or blocks of the image, an adaptive threshold image is determined by sampling a foreground pixel and a background pixel for each initial edge pixel or refined edge pixel, generating histograms for both background and foreground saliency (or gradient magnitude) modulated histograms, determining a threshold range for each block of the image, and interpolating the threshold at each pixel based on the threshold range at each block. Comparing the input image with the resulting locally adaptive threshold image enables extraction of significantly improved foreground.

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San Jose, CA, United States of America

XiuZhong Wang