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1. 发明授权

US10740896B2 Processing of histology images with a convolutional neural network to identify tumors 有权
公开(公告)号：US10740896B2
公开(公告)日：2020-08-11
申请号：US16230820
申请日：2018-12-21
申请人： Leica Biosystems Imaging, Inc.
发明人： Walter Georgescu , Allen Olson , Bharat Annaldas , Darragh Lawler , Kevin Shields , Kiran Saligrama , Mark Gregson
IPC分类号： G06T7/00 , G06K9/00 , G06N3/08 , G06N5/04 , G16H30/20 , G06N20/00 , G06K9/62 , G06K9/46
摘要： 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.

2. 发明授权

US11449998B2 Processing of histology images with a convolutional neural network to identify tumors 有权
公开(公告)号：US11449998B2
公开(公告)日：2022-09-20
申请号：US16986987
申请日：2020-08-06
申请人： Leica Biosystems Imaging, Inc.
发明人： Walter Georgescu , Allen Olson , Bharat Annaldas , Darragh Lawler , Kevin Shields , Kiran Saligrama , Mark Gregson
IPC分类号： G06T7/00 , G06K9/00 , G06N3/08 , G06N5/04 , G16H30/20 , G06N20/00 , G06K9/62 , G06V10/44 , G06V20/69
摘要： 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.

3. 发明授权

US11385713B2 Eye-tracking image viewer for digital pathology 有权
公开(公告)号：US11385713B2
公开(公告)日：2022-07-12
申请号：US17281907
申请日：2019-12-19
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： Allen Olson , Kiran Saligrama
IPC分类号： G06F3/01 , G16H40/67 , G16H30/20 , G06F3/0485 , G16H50/20
摘要： 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.

4. 发明申请

US20200041760A1 REAL-TIME AUTOFOCUS FOCUSING ALGORITHM 审中-公开
公开(公告)号：US20200041760A1
公开(公告)日：2020-02-06
申请号：US16601355
申请日：2019-10-14
申请人： Leica Biosystems Imaging, Inc.
发明人： Allen OLSON , Kiran Saligrama , Yunlu Zou , Peyman Najmabadi
IPC分类号： G02B7/38 , G02B21/36 , G02B7/09 , G02B21/26 , G02B21/02
摘要： 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.

5. 发明授权

US10459193B2 Real-time autofocus focusing algorithm 有权
公开(公告)号：US10459193B2
公开(公告)日：2019-10-29
申请号：US16147448
申请日：2018-09-28
申请人： Leica Biosystems Imaging, Inc.
发明人： Allen Olson , Kiran Saligrama , Yunlu Zou , Peyman Najmabadi
IPC分类号： G02B7/38 , G02B21/02 , G02B21/26 , G02B7/09 , G02B21/36 , H04N5/232
摘要： 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.

6. 发明授权

US11422350B2 Real-time focusing in line scan imaging 有权
公开(公告)号：US11422350B2
公开(公告)日：2022-08-23
申请号：US16818916
申请日：2020-03-13
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： Yunlu Zou , Allen Olson , Kiran Saligrama , Ruby Chen , Peyman Najmabadi , Greg Crandall
IPC分类号： G02B21/24 , G03B13/36 , G02B7/38 , H04N5/232 , G02B21/36 , G02B21/34 , G02B21/26 , G02B21/18 , G02B21/02
摘要： 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.

7. 发明申请

US20220084660A1 ARTIFICIAL INTELLIGENCE PROCESSING SYSTEM AND AUTOMATED PRE-DIAGNOSTIC WORKFLOW FOR DIGITAL PATHOLOGY 有权
公开(公告)号：US20220084660A1
公开(公告)日：2022-03-17
申请号：US17416376
申请日：2020-05-29
申请人： Leica Biosystems Imaging Inc.
发明人： Walter Georgescu , Kiran Saligrama , Carlos Luna , Darragh Lawler , Claude Lacey
IPC分类号： G16H30/40 , G16H30/20 , G16H50/20 , G16H10/60 , G06N3/08
摘要： 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. Advantageously, upon first review by a pathologist, the patient record includes the histological image and results from the automatically ordered additional tests.

8. 发明申请

US20220076411A1 NEURAL NETORK BASED IDENTIFICATION OF AREAS OF INTEREST IN DIGITAL PATHOLOGY IMAGES 有权
公开(公告)号：US20220076411A1
公开(公告)日：2022-03-10
申请号：US17416394
申请日：2020-05-29
申请人： Leica Biosystems Imaging Inc.
发明人： Walter Georgescu , Kiran Saligrama , Allen Olson , Girish Mallya Udupi , Bruno Oliveira
IPC分类号： G06T7/00 , G06K9/00 , G06K9/32 , G16H80/00
摘要： 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. Furthermore, a histological image of a tissue sample of a tissue type that has been treated with a test compound is image processed in order to detect areas where toxic reactions to the test compound may have occurred. An autoencoder is trained with a training data set comprising histological images of tissue samples which are of the given tissue type, but which have not been treated with the test compound. The trained autoencoder is applied to detect tissue areas by their deviation from the normal variation seen in that tissue type as learnt by the training process, and so build up a toxicity map of the image. The toxicity map can then be used to direct a toxicological pathologist to examine the areas identified by the autoencoder as lying outside the normal range of heterogeneity for the tissue type. This makes the pathologists review quicker and more reliable. The toxicity map can also be overlayed with the segmentation mask indicating areas of interest. When an area of interest and an area identified as lying outside the normal range of heterogeneity for the tissue type, and increased confidence score is applied to the overlapping area.

9. 发明授权

US11454781B2 Real-time autofocus focusing algorithm 有权
公开(公告)号：US11454781B2
公开(公告)日：2022-09-27
申请号：US17085962
申请日：2020-10-30
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： Allen Olson , Kiran Saligrama , Yunlu Zou , Peyman Najmabadi
IPC分类号： G02B7/38 , G02B21/02 , G02B21/26 , G02B7/09 , G02B21/36 , H04N5/232
摘要： 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.

10. 发明授权

US10823936B2 Real-time autofocus focusing algorithm 有权
公开(公告)号：US10823936B2
公开(公告)日：2020-11-03
申请号：US16601355
申请日：2019-10-14
申请人： Leica Biosystems Imaging, Inc.
发明人： Allen Olson , Kiran Saligrama , Yunlu Zou , Peyman Najmabadi
IPC分类号： G02B7/38 , G02B21/02 , G02B21/26 , G02B7/09 , G02B21/36 , H04N5/232
摘要： 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.

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