Osaka University

Apsam Imaging Corp.

Kazuaki Nakane

Jun Xu

Anant Madabhushi

Takashi Suzuki

Hirofumi Yamamoto

Naomasa Kawaguchi

Mizuho Nishio

Yao Fu

Nariaki Matsuura

Masahiko Koizumi

Shoichi Tomohisa

Hodaka Numasaki

Michael D Feldman

Marcio Fuzeto Gameiro

Osamu Honda

Masahiro Yanagawa

Noriyuki Tomiyama

Haoda Lu

Sachiko Nankumo

Yasuyoshi Tsutsumi

Akihiro Takiyama

Xiangxue Wang

Chaoyang Yan

Xiangshan Fan

The disclosed feature makes it possible to accurately determine a change that has occurred in a tissue. The feature includes: a binarizing section () that generates, from an image to be analyzed, a plurality of binarized images having respective binarization reference values different from each other; a Betti number calculating section () that calculates, for each of the plurality of binarized images, a one-dimensional Betti number indicating the number of hole-shaped regions each of which is surrounded by pixels each having a first pixel value obtained by binarization and is constituted by pixels each having a second pixel value obtained by binarization; and a determining section () that determines a change that has occurred in the tissue, based on a binarization reference value and a one-dimensional Betti number in a binarized image in which the one-dimensional Betti number is maximized.

Image analysis method, image analysis device, image analysis system, control program, and recording medium

By analyzing an image of a cell sample, whether a cell shown in the image is cancerous or not, the type of cancer, etc. are objectively and appropriately determined. An image analyzing device () includes: a binarizing section () which carries out a binarizing process a plurality of times with respect to a single captured image so as to generate a plurality of binarized images, the binarizing section carrying out the binarizing process each time the binarizing section varies a binarization reference value; a Betti number calculating section () which calculates a region number, which indicates the number of hole-shaped regions, with respect to each of the plurality of binarized images which have been generated; and a cancer determining section () which determines a type of cancer occurring in a cell included in tissue, on the basis of the binarization reference value which has been applied at a time of generation of one of the plurality of binarized images with respect to which one the region number has a given value.

Image analysis method, storage medium, image analysis device, and image analysis system

The degree of differentiation of a cell in tissue is precisely determined. An estimating device () includes: a binarizing section () configured to generate binarized images from an image obtained by capturing an image of tissue; a Betti number calculating section () configured to calculate, for each binarized image, (i) the number of hole-shaped regions (b) each surrounded by pixels of a first pixel value and each composed of pixels of a second pixel value, (ii) the number of connected regions each composed of the pixels of the first pixel value connected together, and (iii) a ratio (R) between (i) and (ii); a statistic calculating section () configured to calculate statistics of the calculated numbers (b, b) and ratio (R); and an estimating section () configured to feed input data including the calculated statistics to a trained estimating model to output the degree of differentiation of the cell in tissue.

Image analysis method, estimating device, estimating system, and storage medium

In order to assess, with high accuracy, a degree of a change having occurred in a structure, the present invention includes: a Betti number calculating section () configured to (I) generate, with respect to a single captured image obtained by capturing an image of a structure, a plurality of binarized images having respective binarization reference values different from each other and (II) calculate, for each of the plurality of binarized images, a first characteristic numerical value indicative of the number of connected regions each of which is obtained by connecting pixels each having one of pixel values obtained by binarization; and a prediction score determining section () configured to determine, in accordance with a result of a comparison, information on a change having occurred in the structure, the comparison having been made between (i) a pattern indicative of a relationship between (a) the respective binarization reference values and (b) the first characteristic numerical value and (ii) a predetermined reference pattern.

Image analysis method, image analysis device, image analysis system, and storage medium

A relationship between constituents of a structure is objectively and appropriately assessed. An image analyzing device includes: a Betti number calculating section () configured to calculate the numbers of holes from respective plurality of binarized images, after the plurality of binarized images are generated by binarizing a single captured image, which is obtained by capturing a structure, while making binarization reference values differ from each other; a function specifying section () configured to specify a characteristic numerical value that characterizes a correspondence between (i) the respective binarization reference values and (ii) the respective numbers of holes; and an output information generating section () configured to generate output information corresponding to the characteristic numerical value.

Image analysis device, image analysis method, image analysis system, and recording medium

A cancer determination apparatus () includes a Betti number calculation section () for calculating, in a captured image (i) in which a tissue is captured and (ii) which has been binarized by use of a prescribed reference value as a threshold, (I) the number of connected components and (II) the number of spaces, each of the connected components being an aggregate of at least one component which is defined by connecting an outer edge(s) of the at least one component which is a graphic having a closed outer edge, each of the spaces being surrounded by an outer edge(s) of the at least one component, a comparison value calculation section () for calculating a comparison value indicating a difference between the number of connected components and the number of spaces, and a determination section () for determining, by comparing the comparison value thus calculated with a predetermined reference value, whether or not an image of a cancer tissue is included in the captured image.

Image analysis apparatus, image analysis method, image analysis program and recording medium

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Suita, Japan

Kazuaki Nakane