Abstract: A system predicts the recurrence of cancer. A first slice of a prostate tissue sample is stained so that luminal epithelial cells and basal epithelial cells are stained different colors. A first digital image is taken of the first slice. The second slice of the sample is stained so that M1 type macrophages and M2 type macrophages are differentially stained. A second digital image is taken of the second slice. The system analyzes the first digital image and defines regions of non-intact glands. Intact gland regions are then determined, and regions of stroma are identified. The system defines influence zones between non-intact regions and stroma regions. Using information from the second image, macrophages in the tissue corresponding to the influence zones are identified and counted. Based at least in part on this count, the system determines a score. The score is indicative of whether the patient will experience PSA recurrence.

Abstract: Both pixel-oriented analysis and the more accurate yet slower object-oriented analysis are used to recognize patterns in images of stained cancer tissue. Images of tissue from other patients that are similar to tissue of a target patient are identified using the standard deviation of color in the images. Object-oriented segmentation is then used to segment small portions of the images of the other patients into object exhibiting object characteristics. Pixelwise descriptors associate each pixel in the remainder of the images with object characteristics based on the color of pixels at predetermined offsets from the characterized pixel. Pixels in the image of the target patient are assigned object characteristics without performing the slow segmentation of the image into objects. A pixel heat map is generated from the target image by assigning pixels the color corresponding to the object characteristic that the pixelwise descriptors indicate is most likely associated with each pixel.

Abstract: Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.

Abstract: A method for identifying blurred areas in digital images of stained tissue involves artificially blurring a learning tile and then training a pixel classifier to correctly classify each pixel as belonging either to the learning tile or to a blurred copy. A learning tile is first selected from a digital image of stained tissue. The learning tile is copied and blurred by applying a filter to each pixel. The pixel classifier is trained to correctly classify each pixel as belonging either to the learning tile or to the blurred, copied learning tile. The pixel classifier then classifies each pixel of the entire digital image as most likely resembling either the learning tile or the blurred learning tile. The digital image is segmented into blurred and unblurred areas based on the pixel classification. The blurred areas and the unblurred areas of the digital image are identified on a graphical user interface.

Abstract: A system predicts the recurrence of cancer. A first slice of a prostate tissue sample is stained so that luminal epithelial cells and basal epithelial cells are stained different colors. A first digital image is taken of the first slice. The second slice of the sample is stained so that M1 type macrophages and M2 type macrophages are differentially stained. A second digital image is taken of the second slice. The system analyzes the first digital image and defines regions of non-intact glands. Intact gland regions are then determined, and regions of stroma are identified. The system defines influence zones between non-intact regions and stroma regions. Using information from the second image, macrophages in the tissue corresponding to the influence zones are identified and counted. Based at least in part on this count, the system determines a score. The score is indicative of whether the patient will experience PSA recurrence.

Abstract: A method for co-registering images of tissue slices stained with different biomarkers displays a first digital image of a first tissue slice on a graphical user interface such that an area of the first image is enclosed by a frame. Then a portion of a second image of a second tissue slice is displayed such that the area of the first image enclosed by the frame is co-registered with the displayed portion of the second image. The displayed portion of the second image has the shape of the frame. The tissue slices are both z slices of a tissue sample taken at corresponding positions in the x and y dimensions. The displayed portion of the second image is shifted in the x and y dimensions to coincide with the area of the first image that is enclosed by the frame as the user shifts the first image under the frame.

Abstract: An analysis and visualization system analyzes a digital image of a tissue sample. In the sample, cells of a first type are stained in a first way, and cells of a second type are stained in a second way. The system segments the high-resolution image into first and second objects representing cells of the first and second types, respectively. The system also identifies a region of interest, and divides it into tiles. The system generates, for each tile, a first value and a second value. The first and second values for a tile are indicative of densities of the first and second objects in the tile. From the values, a measured correlation coefficient (CC) value is determined. The system compares the measured CC value to a reference CC value, thereby obtaining a correspondence value. The system then displays the image region along with a visualization of the correspondence value.

Abstract: Both pixel-oriented analysis and the more accurate yet slower object-oriented analysis are used to recognize patterns in images of stained cancer tissue. Images of tissue from other patients that are similar to tissue of a target patient are identified using the standard deviation of color in the images. Object-oriented segmentation is then used to segment small portions of the images of the other patients into object exhibiting object characteristics. Pixelwise descriptors associate each pixel in the remainder of the images with object characteristics based on the color of pixels at predetermined offsets from the characterized pixel. Pixels in the image of the target patient are assigned object characteristics without performing the slow segmentation of the image into objects. A pixel heat map is generated from the target image by assigning pixels the color corresponding to the object characteristic that the pixelwise descriptors indicate is most likely associated with each pixel.

Abstract: An improved histopathological score is obtained by identifying objects in images of glandular tissue from cancer patients. The objects are identified based on staining by a biomarker. The score predicts that a cancer patient will have a recurrence of cancer of the glandular tissue based on a geometric characteristic of individual identified objects but not on any pattern formed by the identified objects. First objects are generated from the image of glandular tissue which has been stained with a single biomarker that stains epithelial cells. Second objects are then generated using the first objects. A geometric feature of each of the second objects is measured. A shape index is then calculated for each of the second objects based on the geometric feature, and an average shape index is calculated. Based on the average shape index, a score is determined that indicates a level of cancer malignancy of the glandular tissue.

Abstract: A method for generating an image-based test improves diagnostic accuracy by iteratively modifying rule sets governing image and data analysis of coregistered image tiles. Digital images of stained tissue slices are divided into tiles, and tiles from different images are coregistered. First image objects are linked to selected pixels of the tiles. First numerical data is generated by measuring the first objects. Each pixel of a heat map aggregates first numerical data from coregistered tiles. Second objects are linked to selected pixels of the heat map. Measuring the second objects generates second numerical data. The method improves how well second numerical data correlates with clinical data of the patient whose tissue is analyzed by modifying the rule sets used to generate the first and second objects and the first and second numerical data. The test is defined by those rule sets that produce the best correlation with the clinical data.

Abstract: Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.

Abstract: A method for co-registering images of tissue slices stained with different biomarkers displays a first digital image of a first tissue slice on a graphical user interface such that an area of the first image is enclosed by a frame. Then a portion of a second image of a second tissue slice is displayed such that the area of the first image enclosed by the frame is co-registered with the displayed portion of the second image. The displayed portion of the second image has the shape of the frame. The tissue slices are both z slices of a tissue sample taken at corresponding positions in the x and y dimensions. The displayed portion of the second image is shifted in the x and y dimensions to coincide with the area of the first image that is enclosed by the frame as the user shifts the first image under the frame.

Abstract: A method for determining whether a test biomarker is a stain for a type of cell component, such as membrane or nucleus, involves performing various segmentation processes on an image of tissue stained with the test biomarker. One segmentation process searches for a first cell component type, and another segmentation process searches for a second cell component type by segmenting only stained pixels. The test biomarker is identified as a stain for each component type if the process identifies the component based only on stained pixels. Whether the test biomarker is a membrane stain or nucleus stain is displayed on a graphical user interface. In addition, the method identifies stained pixels corresponding to a second cell component using pixels determined to correspond to a first cell component. An expression profile for the test biomarker is then displayed that indicates the proportion of stained pixels in each type of cell component.

Abstract: An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.

Abstract: The coregistration of digital images of tissue slices is improved by updating landmarks based on the manual outlining of regions of interest on the images. A first image of a first slice is coarsely coregistered with a second image of a second slice using a first landmark on the first image and a second landmark on the second image. A user manually outlines a first region of interest on the first image. The outline is positioned over a second region of interest on the second image using the second landmark. The user manually moves a contour point of the outline on the second image to form a corrected outline. The second landmark is moved based on how the contour point was manually moved so that the first and second images are more finely coregistered after the second landmark is moved. Each state of corrected contour points and landmarks is saved.

Abstract: A method for generating an image-based test improves diagnostic accuracy by iteratively modifying rule sets governing image and data analysis of coregistered image tiles. Digital images of stained tissue slices are divided into tiles, and tiles from different images are coregistered. First image objects are linked to selected pixels of the tiles. First numerical data is generated by measuring the first objects. Each pixel of a heat map aggregates first numerical data from coregistered tiles. Second objects are linked to selected pixels of the heat map. Measuring the second objects generates second numerical data. The method improves how well second numerical data correlates with clinical data of the patient whose tissue is analyzed by modifying the rule sets used to generate the first and second objects and the first and second numerical data. The test is defined by those rule sets that produce the best correlation with the clinical data.

Abstract: A system for computer-aided detection uses a computer-implemented network structure to analyze patterns present in digital image slices of a human body and to generate a three-dimensional anatomical model of a patient. The anatomical model is generated by detecting easily identifiable organs first and then using those organs as context objects to detect other organs. A user specifies membership functions that define which objects of the network structure belong to the various classes of human organs specified in a class hierarchy. A membership function of a potentially matching class determines whether a candidate object of the network structure belongs to the potential class based on the relation between a property of the voxels linked to the candidate object and a property of the context object. Some voxel properties used to classify an object are location, brightness and volume. The human organs are then measured to assist in the patient's diagnosis.

Abstract: A method for coregistering images involves defining middle paths through image objects depicting tissue slices of needle biopsies. First landmarks are defined on a first middle path through a first image object in a first digital image of a first tissue slice, and second landmarks are defined on a second middle path through a second image object of a second digital image of a second tissue slice. Individual first landmarks are associated with individual second landmarks. A first pixel in the first object is coregistered with a second pixel in the second object using multiple first and second landmarks. The first image is displayed in a first frame on a graphical user interface, and the second image is displayed in a second frame such that the first pixel is centered in the first frame, the second pixel is centered in the second frame, and the images have the same orientations.

Abstract: A method for determining whether a test biomarker is a stain for a type of cell component, such as membrane or nucleus, involves performing various segmentation processes on an image of tissue stained with the test biomarker. One segmentation process searches for a first cell component type, and another segmentation process searches for a second cell component type by segmenting only stained pixels. The test biomarker is identified as a stain for each component type if the process identifies the component based only on stained pixels. Whether the test biomarker is a membrane stain or nucleus stain is displayed on a graphical user interface. In addition, the method identifies stained pixels corresponding to a second cell component using pixels determined to correspond to a first cell component. An expression profile for the test biomarker is then displayed that indicates the proportion of stained pixels in each type of cell component.

Abstract: A system for computer-aided detection uses a computer-implemented network structure to analyze patterns present in digital image slices of a human body and to generate a three-dimensional anatomical model of a patient. The anatomical model is generated by detecting easily identifiable organs first and then using those organs as context objects to detect other organs. A user specifies membership functions that define which objects of the network structure belong to the various classes of human organs specified in a class hierarchy. A membership function of a potentially matching class determines whether a candidate object of the network structure belongs to the potential class based on the relation between a property of the voxels linked to the candidate object and a property of the context object. Some voxel properties used to classify an object are location, brightness and volume. The human organs are then measured to assist in the patient's diagnosis.