Abstract: A medical image analysis system includes an image receiving/storing unit to receive/store digital images from a slide scanner; an image analysis unit to perform automatic analysis related to tumor cell detection, biomarker quantification and/or pattern recognition, and return analyzed images and/or analysis results; an image evaluation unit with an operation platform and user interface for review of images/analyzed images; an assembly/unification unit having a first interface to the analysis unit, the assembly/unification unit transfering the images to the analysis unit and receiving analyzed images and/or analysis results; a second interface to the evaluation unit, the assembly/unification unit transfering the images/analyzed images and receiving evaluations; a picture archiving/communication system or vendor neutral archive interface, the assembly/unification unit assembling and exporting the images/analyzed images/evaluations to the archiving/communication system or neutral archive.

Abstract: A method of analyzing a plurality of histology slides is provided, wherein possible artefacts are detected at an early stage. This is achieved by including a preliminary imaging and image analysis step in the process flow; the step being executed preferably before any diagnostic assessment e.g. image analysis or manual reading is performed by a pathologist or a lab technician. Accordingly, the histology slides reaching the expert for image analysis are of a higher quality, since the slides are ideally free of artefacts. The method thus saves valuable time for the pathologist, since only artefact-free slides will be subject to a detailed analysis. Furthermore, the method minimizes the risk of misinterpretations leading to potentially false diagnoses. A deep learning model, is also disclosed, which is capable of automatically determining whether histopathological images are suitable for diagnostic and/or research assessment. A training of the deep learning model is also disclosed.

Abstract: A method and a system for training a deep learning model to obtain histopathological information from images.

Abstract: A method and a system for training a deep learning model to obtain histopathological information from images.

Abstract: The quality of staining histological specimens has an impact on the histological assessment of the specimen. One of the causes for variability in the accuracy is the variations in the staining of the specimens to be tested in the working laboratories. The present invention provides a method for assessing the staining quality in a working laboratory and providing a quantitative quality measure for the staining quality. Thereby, it is possible for each working laboratory to evaluate their staining quality and to get feedback of how to improve the quality. The invention further relates to a system and a software program for carrying out the method.

Abstract: The quality of staining histological specimens has an impact on the histological assessment of the specimen. One of the causes for variability in the accuracy is the variations in the staining of the specimens to be tested in the working laboratories. The present invention provides a method for assessing the staining quality in a working laboratory and providing a quantitative quality measure for the staining quality. Thereby, it is possible for each working laboratory to evaluate their staining quality and to get feedback of how to improve the quality. The invention further relates to a system and a software program for carrying out the method.

Abstract: Disclosed is a method and a system for obtaining and analyzing image pairs, obtained as sections of specimen. The invention facilitates registration of two corresponding images, one from each section of the specimen. The invention includes performing a registration process of the two images thereby obtaining a mathematical transformation rule and afterwards using said transformation rule for each image field identified in one image allowing that the corresponding image field in the other image may be identified as well. After the corresponding image pairs have been obtained using the method of the present invention, the sections can be assessed, such as by identifying the counting events for at least one type of object on the image fields within at least one corresponding image pair, optionally using automatic means.

Abstract: Disclosed is a method and a system for obtaining and analyzing image pairs, obtained as sections of specimen. The invention facilitates registration of two corresponding images, one from each section of the specimen. The invention includes performing a registration process of the two images thereby obtaining a mathematical transformation rule and afterwards using said transformation rule for each image field identified in one image allowing that the corresponding image field in the other image may be identified as well. After the corresponding image pairs have been obtained using the method of the present invention, the sections can be assessed, such as by identifying the counting events for at least one type of object on the image fields within at least one corresponding image pair, optionally using automatic means.

Abstract: The present invention relates to methods for identifying and optionally quantifying stained cells in a cell or tissue sample and determining expression level of a target, as well as methods for determining staining intensity and/or staining quality of a stained sample, calibrating digital imaging apparatus using the methods, as well as systems and a computer readable mediums therefore.

Abstract: The present invention relates to a method and a system for obtaining and analysing image pairs, obtained as sections of specimen. The invention facilitates registration of two corresponding images, one from each section of the specimen. The invention includes performing a registration process of the two images thereby obtaining a mathematical transformation rule and afterwards using said transformation rule for each image field identified in one image allowing that the corresponding image field in the other image may be identified as well. After the corresponding image pairs have been obtained using the method of the present invention, the sections can be assessed, such as by identifying the counting events for at least one type of object on the image fields within at least one corresponding image pair, optionally using automatic means.

Abstract: The present invention relates to a method and a system for obtaining and analysing image pairs, obtained as sections of specimen. The invention facilitates registration of two corresponding images, one from each section of the specimen. The invention includes performing a registration process of the two images thereby obtaining a mathematical transformation rule and afterwards using said transformation rule for each image field identified in one image allowing that the corresponding image field in the other image may be identified as well. After the corresponding image pairs have been obtained using the method of the present invention, the sections can be assessed, such as by identifying the counting events for at least one type of object on the image fields within at least one corresponding image pair, optionally using automatic means.

Abstract: The present invention relates to a method and a system for obtaining and analysing image pairs, obtained as sections of specimen. The invention facilitates registration of two corresponding images, one from each section of the specimen. The invention includes performing a registration process of the two images thereby obtaining a mathematical transformation rule and afterwards using said transformation rule for each image field identified in one image allowing that the corresponding image field in the other image may be identified as well. After the corresponding image pairs have been obtained using the method of the present invention, the sections can be assessed, such as by identifying the counting events for at least one type of object on the image fields within at least one corresponding image pair, optionally using automatic means.

Abstract: The present invention relates to methods for identifying and optionally quantifying stained cells in a cell or tissue sample and determining expression level of a target, as well as methods for determining staining intensity and/or staining quality of a stained sample, calibrating digital imaging apparatus using the methods, as well as systems and a computer readable mediums therefore.

Abstract: The present invention relates to a method and a system for obtaining and analysing image pairs, obtained as sections of specimen. The invention facilitates registration of two corresponding images, one from each section of the specimen. The invention includes performing a registration process of the two images thereby obtaining a mathematical transformation rule and afterwards using said transformation rule for each image field identified in one image allowing that the corresponding image field in the other image may be identified as well. After the corresponding image pairs have been obtained using the method of the present invention, the sections can be assessed, such as by identifying the counting events for at least one type of object on the image fields within at least one corresponding image pair, optionally using automatic means.

Abstract: The present invention relates to a method for assessing the presence or absence of lesion(s) in an image and a system therefor, wherein said image may be any image potentially comprising lesions, in particular an image from medical image diagnostics, and more particularly an ocular fundus image. The lesions are identified from starting points being candidate lesion areas and validated with respect to their visibility as compared to the local surroundings.

Abstract: A method of processing an image includes processing the image so as to produce a modified image in which the width of each line object is a single pixel, identifying pixels of the modified image which are relocated on a line object, and allocating to each image point a score value dependent upon the number of adjacent pixels which are likewise located on a line, determining from the score values which of the image points is disassociated with a crossing point or a bifurcation of the respective line object, performing a matching operation on pairs of line segments for each crossing point, classifying the line objects in the original image into two arbitrary sets, and designating one of the sets as representing venous structure, the other of the sets as representing arterial structure.

Abstract: The present invention relates to a method for assessing the presence or absence of lesion(s) in an image and a system therefor, wherein said image may be any image potentially comprising lesions, in particular an image from medical image diagnostics, and more particularly an ocular fundus image. The lesions are identified from starting points being candidate lesion areas and validated with respect to their visibility as compared to the local surroundings.

Abstract: A method for estimating the skeletal status or bone quality of a vertebrate on the basis of two-dimensional image data comprising information relating to the trabecular structure of at least a part of a bone of the vertebrate, the image data being data obtained by exposing at least the part of the bone to electromagnetic radiation, such as X-rays, the method comprising subjecting the image data to a statistical analysis comprising a background correction procedure in which low frequency intensity variations not related to the trabecular structure of the bone are reduced relative to image data related to the trabecular structure of the part of the bone, a feature extraction procedure comprising (a) determining values reflecting the projected trabecular density in the image data, caused by the X-ray attenuating properties of cancellous bone in the part of the bone, for each of a number of locations or areas in the image data, (b) deriving one or more features from the variation of the determined PTD-values, pre

Abstract: A method for estimating the bone quality, or skeletal status of a vertebrate on the basis of two-dimensional image data of cortical bone, and which is determined from a density variation of the cortical bone. Intra cortical resorption, scalloping, spongiosation, striation, periosteal resorption, and/or endosteal resorption caused by osteoporosis, osteopenia or hyperparathyroidism may be determined as oblong density variations detected by filtering the image data with two oblong Gaussian kernels (using a Difference Of Gaussian technique) or at corresponding frequencies in the power spectrum.

Abstract: A method is described for estimating the bone quality of a vertebrate on the basis of two-dimensional image data comprising information relating to the trabecular structure of at least part of a bone of the vertebrate, the image data being data obtained by exposing at least the part of the bone to electromagnetic radiation, the method comprising subjecting the image data to a statistical analysis comprising deriving features from a parametric estimate of a power spectrum and/or deriving features from a transform similar to a distance transform--but in a number of directions.