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1. 发明申请

WO2019133538A2 PROCESSING OF HISTOLOGY IMAGES WITH A CONVOLUTIONAL NEURAL NETWORK TO IDENTIFY TUMORS 审中-公开
公开(公告)号：WO2019133538A2
公开(公告)日：2019-07-04
申请号：PCT/US2018/067349
申请日：2018-12-21
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： GEORGESCU, Walter , OLSON, Allen , ANNALDAS, Bharat , LAWLER, Darragh , SHIELDS, Kevin , SALIGRAMA, Kiran , GREGSON, Mark
IPC分类号： G06T7/00
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. 发明申请

WO2020132358A1 EYE-TRACKING IMAGE VIEWER FOR DIGITAL PATHOLOGY 审中-公开
公开(公告)号：WO2020132358A1
公开(公告)日：2020-06-25
申请号：PCT/US2019/067665
申请日：2019-12-19
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： OLSON, Allen , SALIGRAMA, Kiran
IPC分类号： G06F3/048 , G06F3/0485 , G16H70/60
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.

3. 发明申请

WO2020243556A1 NEURAL NETWORK BASED IDENTIFICATION OF AREAS OF INTEREST IN DIGITAL PATHOLOGY IMAGES 审中-公开
公开(公告)号：WO2020243556A1
公开(公告)日：2020-12-03
申请号：PCT/US2020/035302
申请日：2020-05-29
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： GEORGESCU, Walter , SALIGRAMA, Kiran , OLSON, Allen , MALLYA UDUPI, Girish , OLIVEIRA, Bruno
IPC分类号： G06K9/00 , G06K9/46 , G06K9/62
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 pathologist's 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.

4. 发明申请

WO2019133538A3 PROCESSING OF HISTOLOGY IMAGES WITH A CONVOLUTIONAL NEURAL NETWORK TO IDENTIFY TUMORS 审中-公开
公开(公告)号：WO2019133538A3
公开(公告)日：2019-07-04
申请号：PCT/US2018/067349
申请日：2018-12-21
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： GEORGESCU, Walter , OLSON, Allen , ANNALDAS, Bharat , LAWLER, Darragh , SHIELDS, Kevin , SALIGRAMA, Kiran , GREGSON, Mark
IPC分类号： G06K9/00 , G06K9/20
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.

5. 发明申请

WO2019109024A1 COLOR MONITOR SETTINGS REFRESH 审中-公开
公开(公告)号：WO2019109024A1
公开(公告)日：2019-06-06
申请号：PCT/US2018/063456
申请日：2018-11-30
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： OLSON, Allen , CAMPOS ZURRIAGA, Ruben , JUNG, Reiner
IPC分类号： G06T11/00 , G09G5/02 , H04N1/60
摘要： Described herein are systems and methods that place a known color monitor (known by unique serial number or SKU) into a desired state for displaying digital pathology image data. Using an application programming interface, any color monitor that implements MCCS can be calibrated and characterized immediately before each display of digital pathology image data and can also be periodically reset (if needed) during display of digital pathology image data.

6. 发明申请

WO2017106106A1 AUTOMATIC NUCLEAR SEGMENTATION 审中-公开
标题翻译：自动核分割
公开(公告)号：WO2017106106A1
公开(公告)日：2017-06-22
申请号：PCT/US2016/066200
申请日：2016-12-12
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： GEORGESCU, Walter , SALIGRAMA, Kiran , OLSON, Allen , ANNALDAS, Bharat
IPC分类号： G06T7/11 , G06T7/62 , G06K9/62 , G06K9/80 , G06F19/24
CPC分类号： G06F19/24 , G06K9/00 , G06K9/0014 , G06K9/62 , G06K9/80 , G06T7/11 , G06T7/62 , G06T2207/30024
摘要： Automatic nuclear segmentation. In an embodiment, a plurality of superpixels are determined in a digital image. For each of the superpixels, any superpixels located within a search radius from the superpixel are identified, and, for each unique local combination between the superpixel and any identified superpixels located within the search radius from the superpixel, a local score for the local combination is determined. One of a plurality of global sets of local combinations with an optimum global score is identified based on the determined local scores.
摘要翻译：
自动核分割。在一个实施例中，在数字图像中确定多个超像素。对于每个超像素，标识位于超像素的搜索半径内的任何超像素，并且对于超像素和位于来自超像素的搜索半径内的任何标识的超像素之间的每个唯一局部组合，局部组合的局部分数是决心。基于确定的局部分数来识别具有最佳总体分数的多个局部组合的全局集合之一。

7. 发明申请

WO2017053891A1 REAL-TIME FOCUSING IN LINE SCAN IMAGING 审中-公开
标题翻译：实时聚焦在线扫描成像
公开(公告)号：WO2017053891A1
公开(公告)日：2017-03-30
申请号：PCT/US2016/053581
申请日：2016-09-23
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： ZOU, Yunlu , OLSON, Allen , SALIGRAMA, Kiran , CHEN, Ruby , NAJMABADI, Peyman , CRANDALL, Greg
IPC分类号： G03B13/00 , G01B9/00 , G02B7/28 , G02B21/02 , G02B21/06 , G02B27/40
CPC分类号： G02B27/40 , G02B7/28 , G02B21/02 , G02B21/06 , G03B13/36
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.
摘要翻译：用于在显微镜载玻片上获取样品的数字图像的系统。在一个实施例中，系统包括被配置为支撑样本的台，具有与舞台正交的单个光轴的物镜，成像传感器和聚焦传感器。该系统还包括光学耦合到物镜并被配置为接收与物镜的光轴相对应的视场的至少一个分束器，并且同时向成像传感器提供视野的至少第一部分以及视场的至少第二部分到聚焦传感器。聚焦传感器可以同时获取多个不同焦距的图像和/或同时获取一对镜像图像，每个镜像图像包括以多个不同焦距获取的像素。

8. 发明申请

WO2017049226A1 AUTOMATED STAIN FINDING IN PATHOLOGY BRIGHT-FIELD IMAGES 审中-公开
标题翻译：自动化的斑块在病理学领域中的发现
公开(公告)号：WO2017049226A1
公开(公告)日：2017-03-23
申请号：PCT/US2016/052331
申请日：2016-09-16
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： GEORGESCU, Walter , ANNALDAS, Bharat , OLSON, Allen , SALIGRAMA, Kiran
IPC分类号： G06K9/00
CPC分类号： G06K9/00127 , G06K9/00147
摘要： 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.
摘要翻译：自动染色。在一个实施例中，接收包含一个或多个污渍的样品的图像。对于图像中的多个像素中的每一个，确定像素的光密度矢量。光密度矢量包括一个或多个污渍中的每一个的值，并且表示光密度空间中具有等于一个或多个污渍数量的维数的点。光密度矢量从光密度空间变换为较低维空间中的表示。较低的维数空间具有等于光密度空间的维数的一个维数的数量。基于该表示来识别对应于一个或多个污渍中的每一个的光密度矢量。

9. 发明申请

WO2022271560A1 SYSTEM AND METHOD FOR QUALITY ASSURANCE OF PIXEL PIPELINE 审中-公开
公开(公告)号：WO2022271560A1
公开(公告)日：2022-12-29
申请号：PCT/US2022/034032
申请日：2022-06-17
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： OLSON, Allen , CAMARATTA, Chris
IPC分类号： G09G5/10 , G09G2320/04 , G09G2320/06 , G09G2320/0626 , G09G2320/066 , G09G2320/0693 , G09G2320/103 , G09G2360/06 , G09G2370/02 , G09G2380/08
摘要： Methods and systems method for determining quality of an image after a system configuration change. A method includes receiving an indication of a configuration update of an imaging system, processing a test image using the image display system to generate updated test image pixel data for display on the imaging system, determining if the updated test image pixel data is different than the ground truth pixel data of the test image by more than a quality threshold, and in response to the updated test image pixel data being different than the ground truth pixel data of the test image by more than the quality threshold, generate in indicator that the configuration change adversely affected the image generation properties of the imaging system.

10. 发明申请

WO2021026278A1 REAL-TIME FOCUSING IN A SLIDE-SCANNING SYSTEM 审中-公开
公开(公告)号：WO2021026278A1
公开(公告)日：2021-02-11
申请号：PCT/US2020/045074
申请日：2020-08-05
申请人： LEICA BIOSYSTEMS IMAGING, INC.
发明人： OLSON, Allen , ZOU, Yunlu , ANNALDAS, Bharat , CHEN, Leng-Chun
IPC分类号： G02B7/28 , G02B21/00 , G02B21/24 , G02B21/36 , G06K9/00 , H04N5/369 , G02B7/38 , G03B13/36 , H04N5/232
摘要： Real-time focusing in a slide-scanning system. In an embodiment, focus points are added to an initialized focus map while acquiring a plurality of image stripes of a sample on a glass slide. For each image stripe, a plurality of frames, collectively representing the image stripe, may be acquired using both an imaging line-scan camera and a tilted focusing line-scan camera. Focus points, representing positions of best focus for trusted frames, are added to the focus map. Outlying focus points are removed from the focus map. In some cases, one or more image stripes may be reacquired. Finally, the image stripes are assembled into a composite image of the sample.

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