Nec Corporation

Yoshiko Yoshihara

Tomoharu Kiyuna

Akira Saito

Maki Sano

Kenji Okajima

Toru Sano

Brandon Malone

Kousuke Onoue

A method of ranking epitopes derived from neoantigens as targets for personalized immunotherapy includes collecting candidate epitopes based on patient data of a cancer patient. A set of scores are calculated for each of the candidate epitopes, each of the scores in a respective one of the sets for a respective one of the candidate epitopes representing an independent measure of a likelihood of the respective one of candidate epitopes to elicit an immune response in the cancer patient. The scores in each of the sets of scores are combined into a single score for each of the candidate epitopes. The single scores for the candidate epitopes in each case reflect an overall likelihood of eliciting the immune response in the patient. The candidate epitopes are ranked using the single scores for the immunotherapy.

Method and system of targeting epitopes for neoantigen-based immunotherapy

When making a diagnosis by synthetically taking into account the information obtained by preparing a plurality of samples using different staining methods or the like and sequentially observing the samples, a diagnostician must remember information about the plurality of samples. The present invention includes image storage means for storing a reference image obtained by photographing a predetermined stained sample of and a comparison image obtained by photographing another stained sample of substantially identical biological tissues, partial image creation means for detecting a structure of the biological tissue from the reference image, cutting out from the comparison image a partial image that contains the structure detected, and storing into partial image storage means the partial image in association with the structure and the comparison image, image output means for outputting a result image that includes the reference image and the partial image, and the partial image storage means.

Diagnosis assisting system, diagnosis assisting method, and program thereof

The present invention provides an information processing apparatus capable of accurately separating parenchymal cells and stromal cells from each other regardless of the staining intensity of the cells. The information processing apparatus is an information processing apparatusincluding: an image processing unitfor smoothing a tissue sample imageobtained by staining and then imaging a biological tissue containing parenchymal cellsand stromal cellsso that luminance values of cell components of each of the parenchymal cellsbecome less than those of each of the stromal cells; and a mask generation unitfor generating, through generating a binary image by binarizing the tissue sample imagesmoothed by the image processing unit, a maskfor removing a region of the stromal cells from the tissue sample image

Information processing apparatus, information processing system, information processing method, program, and recording medium

Luminance calculation meanscalculates luminance of a cell nucleus extracted from an antinuclear antibody image of a designated dilution ratio. Titer prediction meanspredicts an antinuclear antibody titer, using a staining pattern specified from a staining form of the cell nucleus and the luminance at at least one dilution ratio.

Antinuclear antibody image analysis system, antinuclear antibody image analysis method, and antinuclear antibody image analysis program

The present invention provides an information processing apparatus capable of selecting a region of cancer cells in any tissue sample image and easily and accurately counting the number of the cancer cells. The information processing apparatus is an information processing apparatusincluding: an acquisition unitfor acquiring image data obtained by reading a tissue sample imageobtained by putting a markspecifying a selected areaon an image obtained by immunostaining and then imaging a biological tissue; and a counting unitfor counting the number of cancer cells in the selected areaspecified by the markbased on the image data of the tissue sample imageacquired by the acquisition unit, wherein a diagnosis based on the tissue sample imageis supported.

A partial image extracting unit extracts images of a predetermined size and constant magnification from a tissue region. A mask generating unit generates a mask for removing a region not intended for measurement from the tissue region for each extracted image. A complete mask generating unit generates a temporary complete mask in which the masks generated for each of the images are integrated together, and generates a complete mask in which close portions among unmasked portions in the temporary complete mask are unified into one or more target regions. A measuring unit measures information pertaining to an object to be measured included in the image, and this information is measured for each of the images extracted by the partial image extracting unit. A region information calculating unit calculates, for each target region, information pertaining to the object to be measured from the measured information and from the complete mask.

Image measurement apparatus and image measurment method measuring the cell neclei count

Information processing apparatus, information processing system, information processing method, program and recording medium

A support system for histopathological diagnosis includes a cell nucleus uniformity evaluation unit evaluating a uniformity of a plurality of cell nuclei included in a ductal region in an image. With this configuration, there is provided a support system, a support method and a support program for histopathological diagnosis, which enables realization of highly accurate cancer differentiation in a pathological diagnosis.

Support system for histopathological diagnosis, support program for histopathological diagnosis and support method for histopathological diagnosis

This invention is directed to improvement of the accuracy of cancer differentiation in the pathologic diagnosis using a pathologic tissue image analysis. There are included a stroma area/duct area detecting module for detecting, from a biological tissue image, a duct area on the basis of duct characteristic information indicating the characteristic of ducts included in a tissue area in the biological tissue image; an intraduct cell-nucleus detecting module for extracting, based on a predetermined pigment reference, cell nucleuses included in the detected duct area; and a duct formation evaluating module for calculating a heterotypic degree of each duct on the basis of the distribution state of the cell nucleuses for a predetermined base areas in each duct.

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Tokyo, Japan

Yoshiko Yoshihara