Abstract: Image viewing in digital pathology using eye-tracking. In an embodiment, a position of a user's gaze on a graphical user interface, comprising at least a portion of a digital slide image within a macro view, is repeatedly detected based on an output from an eye-tracking device. After detecting a change of the user's gaze from a first position to a second position on the graphical user interface, a view of the digital slide image within the macro view is automatically panned based on the second position, so as to move a position on the digital slide image that corresponds to the second position on the graphical user interface toward a center of the macro view.

Abstract: Image viewing in digital pathology using eye-tracking. In an embodiment, a position of a user's gaze on a graphical user interface, comprising at least a portion of a digital slide image within a macro view, is repeatedly detected based on an output from an eye-tracking device. After detecting a change of the user's gaze from a first position to a second position on the graphical user interface, a view of the digital slide image within the macro view is automatically panned based on the second position, so as to move a position on the digital slide image that corresponds to the second position on the graphical user interface toward a center of the macro view.

Abstract: A digital scanning apparatus is provided that includes imaging and focusing sensors and a processor to analyze the image data captured by the imaging and focusing sensors and adjust the focus of the scanning apparatus in real time during a scanning operation. The individual pixels of the imaging sensor are all in the same image plane with respect to the optical path of the digital scanning apparatus. The individual pixels of the focusing sensor are each in a different image plane with respect to the optical path, and one pixel of the focusing sensor is on the same image plane as the image sensor. The processor analyzes image data from the imaging sensor and the focusing sensor and determines a distance and direction to adjust the relative position of an objective lens and a stage of the digital scanning apparatus to achieve optimal focus during the scanning operation.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.

Abstract: System for acquiring a digital image of a sample on a microscope slide. In an embodiment, the system comprises a stage configured to support a sample, an objective lens having a single optical axis that is orthogonal to the stage, an imaging sensor, and a focusing sensor. The system further comprises at least one beam splitter optically coupled to the objective lens and configured to receive a field of view corresponding to the optical axis of the objective lens, and simultaneously provide at least a first portion of the field of view to the imaging sensor and at least a second portion of the field of view to the focusing sensor. The focusing sensor may simultaneously acquire image(s) at a plurality of different focal distances and/or simultaneously acquire a pair of mirrored images, each comprising pixels acquired at a plurality of different focal distances.

Abstract: Automated stain finding. In an embodiment, an image of a sample comprising one or more stains is received. For each of a plurality of pixels in the image, an optical density vector for the pixel is determined. The optical density vector comprises a value for each of the one or more stains, and represents a point in an optical density space that has a number of dimensions equal to a number of the one or more stains. The optical density vectors are transformed from the optical density space into a representation in a lower dimensional space. The lower dimensional space has a number of dimensions equal to one less than the number of dimensions of the optical density space. An optical density vector corresponding to each of the one or more stains is identified based on the representation.

Abstract: Image viewing in digital pathology using eye-tracking. In an embodiment, a position of a user's gaze on a graphical user interface, comprising at least a portion of a digital slide image within a macro view, is repeatedly detected based on an output from an eye-tracking device. After detecting a change of the user's gaze from a first position to a second position on the graphical user interface, a view of the digital slide image within the macro view is automatically panned based on the second position, so as to move a position on the digital slide image that corresponds to the second position on the graphical user interface toward a center of the macro view.

Abstract: A digital pathology system comprising an AI processing module configured to invoke an instance of an AI processing application for processing image data from a histological image and an application module configured to invoke an instance of an application operable to perform an image processing task on a histological image associated with a patient record, wherein the image processing task includes an AI element. The application creates processing jobs to handle the AI elements of its task which are handled by the AI processing module. The AI processing module may be a CNN that processes a histological image to identify tumors by classifying image pixels into one of multiple tissue classes of tumorous or non-tumorous tissue. A test ordering module automatically determines based on identified tissue classes whether additional tests should be performed on the tissue sample. For each additional test, an order is automatically created and submitted.

Abstract: A CNN is applied to a histological image to identify areas of interest. The CNN classifies pixels according to relevance classes including one or more classes indicating levels of interest and at least one class indicating lack of interest. The CNN is trained on a training data set including data which has recorded how pathologists have interacted with visualizations of histological images. In the trained CNN, the interest-based pixel classification is used to generate a segmentation mask that defines areas of interest. The mask can be used to indicate where in an image clinically relevant features may be located. Further, it can be used to guide variable data compression of the histological image. Moreover, it can be used to control loading of image data in either a client-server model or within a memory cache policy.

Abstract: Image viewing in digital pathology using eye-tracking In an embodiment, a position of a user's gaze on a graphical user interface, comprising at least a portion of a digital slide image within a macro view, is repeatedly detected based on an output from an eye-tracking device. After detecting a change of the user's gaze from a first position to a second position on the graphical user interface, a view of the digital slide image within the macro view is automatically panned based on the second position, so as to move a position on the digital slide image that corresponds to the second position on the graphical user interface toward a center of the macro view.

Abstract: A digital scanning apparatus is provided that includes imaging and focusing sensors and a processor to analyze the image data captured by the imaging and focusing sensors and adjust the focus of the scanning apparatus in real time during a scanning operation. The individual pixels of the imaging sensor are all in the same image plane with respect to the optical path of the digital scanning apparatus. The individual pixels of the focusing sensor are each in a different image plane with respect to the optical path, and one pixel of the focusing sensor is on the same image plane as the image sensor. The processor analyzes image data from the imaging sensor and the focusing sensor and determines a distance and direction to adjust the relative position of an objective lens and a stage of the digital scanning apparatus to achieve optimal focus during the scanning operation.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.

Abstract: A digital scanning apparatus is provided that includes imaging and focusing sensors and a processor to analyze the image data captured by the imaging and focusing sensors and adjust the focus of the scanning apparatus in real time during a scanning operation. The individual pixels of the imaging sensor are all in the same image plane with respect to the optical path of the digital scanning apparatus. The individual pixels of the focusing sensor are each in a different image plane with respect to the optical path, and one pixel of the focusing sensor is on the same image plane as the image sensor. The processor analyzes image data from the imaging sensor and the focusing sensor and determines a distance and direction to adjust the relative position of an objective lens and a stage of the digital scanning apparatus to achieve optimal focus during the scanning operation.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.

Abstract: System for acquiring a digital image of a sample on a microscope slide. In an embodiment, the system comprises a stage configured to support a sample, an objective lens having a single optical axis that is orthogonal to the stage, an imaging sensor, and a focusing sensor. The system further comprises at least one beam splitter optically coupled to the objective lens and configured to receive a field of view corresponding to the optical axis of the objective lens, and simultaneously provide at least a first portion of the field of view to the imaging sensor and at least a second portion of the field of view to the focusing sensor. The focusing sensor may simultaneously acquire image(s) at a plurality of different focal distances and/or simultaneously acquire a pair of mirrored images, each comprising pixels acquired at a plurality of different focal distances.

Abstract: System for acquiring a digital image of a sample on a microscope slide. In an embodiment, the system comprises a stage configured to support a sample, an objective lens having a single optical axis that is orthogonal to the stage, an imaging sensor, and a focusing sensor. The system further comprises at least one beam splitter optically coupled to the objective lens and configured to receive a field of view corresponding to the optical axis of the objective lens, and simultaneously provide at least a first portion of the field of view to the imaging sensor and at least a second portion of the field of view to the focusing sensor. The focusing sensor may simultaneously acquire image(s) at a plurality of different focal distances and/or simultaneously acquire a pair of mirrored images, each comprising pixels acquired at a plurality of different focal distances.

Abstract: System for acquiring a digital image of a sample on a microscope slide. In an embodiment, the system comprises a stage configured to support a sample, an objective lens having a single optical axis that is orthogonal to the stage, an imaging sensor, and a focusing sensor. The system further comprises at least one beam splitter optically coupled to the objective lens and configured to receive a field of view corresponding to the optical axis of the objective lens, and simultaneously provide at least a first portion of the field of view to the imaging sensor and at least a second portion of the field of view to the focusing sensor. The focusing sensor may simultaneously acquire image(s) at a plurality of different focal distances and/or simultaneously acquire a pair of mirrored images, each comprising pixels acquired at a plurality of different focal distances.

Abstract: A digital scanning apparatus is provided that includes imaging and focusing sensors and a processor to analyze the image data captured by the imaging and focusing sensors and adjust the focus of the scanning apparatus in real time during a scanning operation. The individual pixels of the imaging sensor are all in the same image plane with respect to the optical path of the digital scanning apparatus. The individual pixels of the focusing sensor are each in a different image plane with respect to the optical path, and one pixel of the focusing sensor is on the same image plane as the image sensor. The processor analyzes image data from the imaging sensor and the focusing sensor and determines a distance and direction to adjust the relative position of an objective lens and a stage of the digital scanning apparatus to achieve optimal focus during the scanning operation.

Abstract: A digital scanning apparatus is provided that includes imaging and focusing sensors and a processor to analyze the image data captured by the imaging and focusing sensors and adjust the focus of the scanning apparatus in real time during a scanning operation. The individual pixels of the imaging sensor are all in the same image plane with respect to the optical path of the digital scanning apparatus. The individual pixels of the focusing sensor are each in a different image plane with respect to the optical path, and one pixel of the focusing sensor is on the same image plane as the image sensor. The processor analyzes image data from the imaging sensor and the focusing sensor and determines a distance and direction to adjust the relative position of an objective lens and a stage of the digital scanning apparatus to achieve optimal focus during the scanning operation.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.