Pathological diagnosis support device, program, method, and system
First Claim
1. A pathological diagnosis support device, comprising:
- learning pattern input means for obtaining images centered on a tumor from a pathological image and inputting thereto the images as learning patterns;
learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
feature candidate generator means for generating a plurality of feature candidates;
feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means;
feature storage means for storing and keeping the set of features determined by the feature determining means;
category table generator means for generating a category table;
pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns;
feature extracting means for extracting features from the input patterns; and
diagnosis means for conducting diagnosis using the features, wherein;
the feature determining means calculates a feature of each of the learning patterns corresponding to each of the feature candidates and determines as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value; and
sequentially determines, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value;
the category table generator means calculates each feature of each of the learning patterns using the feature set and classifies the patterns using the category table including each feature of the learning patterns and the class information; and
the feature extracting means calculates each feature of the input patterns using the feature set,wherein the learning pattern input means and the pattern input means select, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs, calculate distance between a center of distribution of the color region and each pixel belonging to the color region, assign a signal to the pixel according to the distance, detect a peak of distribution of the signals in the pathological image, and input an image centered on the peak as the learning pattern.
1 Assignment
0 Petitions
Accused Products
Abstract
A pathological diagnosis support device, a pathological diagnosis support program, a pathological diagnosis support method, and a pathological diagnosis support system extract a pathological tissue for diagnosis from a pathological image and diagnose the pathological tissue. A tissue collected in a pathological inspection is stained using, for example, hematoxylin and eosin. In consideration of the state of the tissue in which a cell nucleus and its peripheral constituent items are stained in respective colors unique thereto, subimages such as a cell nucleus, a pore, cytoplasm, interstitium are extracted from the pathological image, and color information of the cell nucleus is also extracted. The subimages and the color information are stored as feature candidates so that presence or absence of a tumor and benignity or malignity of the tumor are determined.
-
Citations
40 Claims
-
1. A pathological diagnosis support device, comprising:
-
learning pattern input means for obtaining images centered on a tumor from a pathological image and inputting thereto the images as learning patterns; learning pattern storage means for storing and keeping the learning patterns to which class information is attached; feature candidate generator means for generating a plurality of feature candidates; feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means; feature storage means for storing and keeping the set of features determined by the feature determining means; category table generator means for generating a category table; pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns; feature extracting means for extracting features from the input patterns; and diagnosis means for conducting diagnosis using the features, wherein; the feature determining means calculates a feature of each of the learning patterns corresponding to each of the feature candidates and determines as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value; and
sequentially determines, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value;the category table generator means calculates each feature of each of the learning patterns using the feature set and classifies the patterns using the category table including each feature of the learning patterns and the class information; and the feature extracting means calculates each feature of the input patterns using the feature set, wherein the learning pattern input means and the pattern input means select, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs, calculate distance between a center of distribution of the color region and each pixel belonging to the color region, assign a signal to the pixel according to the distance, detect a peak of distribution of the signals in the pathological image, and input an image centered on the peak as the learning pattern. - View Dependent Claims (2, 3, 4, 5, 6, 9, 10, 11, 12, 37, 38, 39, 40)
-
-
7. A pathological diagnosis support device comprising:
-
learning pattern input means for obtaining images centered on a tumor from a pathological image and inputting thereto the images as learning patterns; learning pattern storage means for storing and keeping the learning patterns to which class information is attached; feature candidate generator means for generating a plurality of feature candidates; feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means; feature storage means for storing and keeping the set of features determined by the feature determining means; category table generator means for generating a category table; pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns; feature extracting means for extracting features from the input patterns; and diagnosis means for conducting diagnosis using the features, wherein; the feature determining means calculates a feature of each of the learning patterns corresponding to each of the feature candidates and determines as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value; and
sequentially determines, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value;the category table generator means calculates each feature of each of the learning patterns using the feature set and classifies the patterns using the category table including each feature of the learning patterns and the class information; and the feature extracting means calculates each feature of the input patterns using the feature set, wherein the feature candidates generated by the feature generator means includes a feature function discriminating a color of the tumor, and wherein the feature determining means compares the signal of each pixel included in the learning patterns calculated by the learning pattern input means with a predetermined threshold value; and wherein the learning pattern input means and the pattern input means select, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs, calculate distance between a center of distribution of the color region and each pixel belonging to the color region, assign a signal to the pixel according to the distance, detect a peak of distribution of the signals in the pathological image, and input an image centered on the peak as the learning pattern.
-
-
8. A pathological diagnosis support device comprising:
-
learning pattern input means for obtaining images centered on a tumor from a pathological image and inputting thereto the images as learning patterns; learning pattern storage means for storing and keeping the learning patterns to which class information is attached; feature candidate generator means for generating a plurality of feature candidates; feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means; feature storage means for storing and keeping the set of features determined by the feature determining means; category table generator means for generating a category table; pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns; feature extracting means for extracting features from the input patterns; and diagnosis means for conducting diagnosis using the features, wherein; the feature determining means calculates a feature of each of the learning patterns corresponding to each of the feature candidates and determines as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value; and
sequentially determines, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value;the category table generator means calculates each feature of each of the learning patterns using the feature set and classifies the patterns using the category table including each feature of the learning patterns and the class information; and the feature extracting means calculates each feature of the input patterns using the feature set, wherein the feature candidates generated by the feature generator means includes a feature function discriminating a color of the tumor, and wherein the feature determining means compares the signal of each pixel included in the learning patterns calculated by the learning pattern input means with a mean value of signals of pixels in the proximity of the pixel; and wherein the learning pattern input means and the pattern input means select, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs, calculate distance between a center of distribution of the color region and each pixel belonging to the color region, assign a signal to the pixel according to the distance, detect a peak of distribution of the signals in the pathological image, and input an image centered on the peak as the learning pattern.
-
-
13. A pathological diagnosis support program on computer readable medium for use with a pathological diagnosis support device comprising learning pattern input means for obtaining from a pathological image, images centered on a tumor and inputting thereto the images as learning patterns;
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
feature candidate generator means for generating a plurality of feature candidates;
feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means;
feature storage means for storing and keeping the set of features determined by the feature determining means;
category table generator means for generating a category table;
pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns;
feature extracting means for extracting features from the input patterns; and
diagnosis means for conducting diagnosis using the features, said program comprising instructions that cause the pathological diagnosis support device to perform steps of;calculating, using the feature determining means, a feature of each of the learning patterns corresponding to each of the feature candidates and determining as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value, and sequentially determining, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value; calculating, using the category table generator means, each feature of each of the learning patterns using the feature set and classifying the patterns using the category table including each feature of the learning patterns and the class information; calculating, using the feature extracting means, each feature of the input patterns using the feature set; processing to select, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs; processing to calculate distance between a center of distribution of the color region and each pixel belonging to the color region; processing to assign a signal to the pixel according to the distance; processing to detect a peak of distribution of the signals in the pathological image; and processing to input an image centered on the peak as the learning pattern. - View Dependent Claims (14, 15, 16, 17, 18, 21, 22, 23, 24)
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
-
19. A pathological diagnosis support program, on computer readable medium for use with a pathological diagnosis support device comprising learning pattern input means for obtaining from a pathological image, images centered on a tumor and inputting thereto the images as learning patterns;
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
feature candidate generator means for generating a plurality of feature candidates;
feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means;
feature storage means for storing and keeping the set of features determined by the feature determining means;
category table generator means for generating a category table;
pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns;
feature extracting means for extracting features from the input patterns; and
diagnosis means for conducting diagnosis using the features, said program comprising instructions that cause the pathological diagnosis support device to perform steps of;calculating, using the feature determining means, a feature of each of the learning patterns corresponding to each of the feature candidates and determining as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value, and sequentially determining, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value; calculating, using the category table generator means, each feature of each of the learning patterns using the feature set and classifying the patterns using the category table including each feature of the learning patterns and the class information; calculating, using the feature extracting means, each feature of the input patterns using the feature set; comparing, using the feature determining means, the signal of each pixel included in the learning patterns calculated by the learning pattern input means with a predetermined threshold value; selecting, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs; calculating distance between a center of distribution of the color region and each pixel belonging to the color region; assigning a signal to the pixel according to the distance; detecting a peak of distribution of the signals in the pathological image; and inputting an image centered on the peak as the learning pattern, wherein the feature candidates generated by the feature generator means includes a feature function discriminating a color of the tumor.
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
-
20. A pathological diagnosis support program on computer readable medium for use with a pathological diagnosis support device comprising learning pattern input means for obtaining from a pathological image, images centered on a tumor and inputting thereto the images as learning patterns;
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
feature candidate generator means for generating a plurality of feature candidates;
feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means;
feature storage means for storing and keeping the set of features determined by the feature determining means;
category table generator means for generating a category table;
pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns;
feature extracting means for extracting features from the input patterns; and
diagnosis means for conducting diagnosis using the features, said program comprising instructions that cause the pathological diagnosis support device to perform steps of;calculating, using the feature determining means, a feature of each of the learning patterns corresponding to each of the feature candidates and determining as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value, and sequentially determining, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value; calculating, using the category table generator means, each feature of each of the learning patterns using the feature set and classifying the patterns using the category table including each feature of the learning patterns and the class information; calculating, using the feature extracting means, each feature of the input patterns using the feature set; comparing, using the feature determining means, the signal of each pixel included in the learning patterns calculated by the learning pattern input means with a mean value of signals of pixels in the proximity of the pixel; selecting, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belong; calculating distance between a center of distribution of the color region and each pixel belonging to the color region; assigning a signal to the pixel according to the distance; detecting a peak of distribution of the signals in the pathological image; and inputting an image centered on the peak as the learning pattern, wherein the feature candidates generated by the feature generating means includes a feature function discriminating a color of the tumor.
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
-
25. A pathological diagnosis support method for use with a pathological diagnosis support device comprising learning pattern input means for obtaining from a pathological image to be used for learning, images centered on a tumor and inputting thereto the images as learning patterns;
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
feature candidate generator means for generating a plurality of feature candidates;
feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means;
feature storage means for storing and keeping the set of features determined by the feature determining means;
category table generator means for generating a category table;
pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns;
feature extracting means for extracting features from the input patterns; and
diagnosis means for conducting diagnosis using the features, the method comprising;
using a processors to perform steps ofa first step in which the feature determining means calculates a feature of each of the learning patterns corresponding to each of the feature candidates and determines as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value; and
sequentially determines, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value;a second step in which the category table generator means calculates each feature of each of the learning patterns using the feature set and classifies the patterns using the category table including each feature of the learning patterns and the class information; a third step in which the feature extracting means calculates each feature of the input patterns using the feature set; and steps to be executed by the learning pattern input means and the pattern input means, the steps including; a step of selecting, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs; a step of calculating distance between a center of distribution of the color region and each pixel belonging to the color region; a step of assigning a signal to the pixel according to the distance; a step of detecting a peak of distribution of the signals in the pathological image; and a step of inputting an image centered on the peak as the learning pattern. - View Dependent Claims (26, 27, 28, 29, 30, 33, 34, 35, 36)
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
-
31. A pathological diagnosis support method comprising learning pattern input means for obtaining from a pathological image to be used for learning, images centered on a tumor and inputting thereto the images as learning patterns;
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
feature candidate generator means for generating a plurality of feature candidates;
feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means;
feature storage means for storing and keeping the set of features determined by the feature determining means;
category table generator means for generating a category table;
pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns;
feature extracting means for extracting features from the input patterns; and
diagnosis means for conducting diagnosis using the features, the method comprising;
using a processors to perform steps ofa first step in which the feature determining means calculates a feature of each of the learning patterns corresponding to each of the feature candidates and determines as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value; and
sequentially determines, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value;a second step in which the category table generator means calculates each feature of each of the learning patterns using the feature set and classifies the patterns using the category table including each feature of the learning patterns and the class information; a third step in which the feature extra6ting means calculates each feature of the input patterns using the feature set; a fourth step in which the feature determining means compares the signal of each pixel included in the learning patterns calculated by the learning pattern input means with a predetermined threshold value; a step of selecting from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs; a step of calculating distance between a center of distribution of the color region and each pixel belonging to the color region; a step of assigning a signal to the pixel according to the distance; a step of detecting a peak of distribution of the signals in the pathological image; and a step of inputting an image centered on the peak as the learning pattern, wherein the feature candidates generated by the feature generator means includes a feature function discriminating a color of the tumor.
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
-
32. A pathological diagnosis support method, comprising learning pattern input means for obtaining from a pathological image to be used for learning, images centered on a tumor and inputting thereto the images as learning patterns;
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
feature candidate generator means for generating a plurality of feature candidates;
feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means;
feature storage means for storing and keeping the set of features determined by the feature determining means;
category table generator means for generating a category table;
pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns;
feature extracting means for extracting features from the input patterns; and
diagnosis means for conducting diagnosis using the features, the method comprising;
using a processors to perform steps ofa first step in which the feature determining means calculates a feature of each of the learning patterns corresponding to each of the feature candidates and determines as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value; and
sequentially determines, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value;a second step in which the category table generator means calculates each feature of each of the learning patterns using the feature set and classifies the patterns using the category table including each feature of the learning patterns and the class information; a third step in which the feature extracting means calculates each feature of the input patterns using the feature set; a fourth step in which the feature determining means compares the signal of each pixel included in the learning patterns calculated by the learning pattern input means with a mean value of signals of pixels in the proximity of the pixel; a step of selecting, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs; a step of calculating distance between a center of distribution of the color region and each pixel belonging to the color region; a step of assigning a signal to the pixel according to the distance; a step of detecting a peak of distribution of the signals in the pathological image; and a step of inputting an image centered on the peak as the learning pattern, wherein the feature candidates generated by the feature generator means includes a feature function discriminating a color of the tumor.
- learning pattern storage means for storing and keeping the learning patterns to which class information is attached;
Specification