公开(公告)号：US10529453B2

公开(公告)日：2020-01-07

申请号：US15663819

申请日：2017-07-31

申请人： Definiens AG

发明人： Kai Hartmann ,  Florian Leiss ,  Guenter Schmidt

IPC分类号： G16H50/30 ,  G06F19/00 ,  G06K9/00 ,  A61B5/00 ,  G01N33/574 ,  G06T7/00

摘要： A novel cancer scoring tool not only generates a score, but it also generates a confidence number. The tool receives a digital image of tissue of a patient. The tool identifies cell objects in the image and from that determines a first score. The magnitude of this first score is indicative of the severity of cancer in the tissue of the patient. The tool uses an overall false negative rate value and an overall false positive rate value to generate a set of second scores. The rate values are determined from training information. From the second scores, the tool determines the confidence number. The confidence number indicates the confidence the tool has in the first score being correct. The first score and an indication of the confidence number and the digital image are all displayed together on the display of the tool.

公开(公告)号：US20190205760A1

公开(公告)日：2019-07-04

申请号：US16221403

申请日：2018-12-14

申请人： Definiens AG

发明人： Tobias Wiestler ,  Simon Lanzmich ,  Nicolas Brieu ,  Guenter Schmidt ,  Moritz Widmaier

IPC分类号： G06N3/08 ,  G06N5/04 ,  G06N20/10 ,  G06T7/00 ,  G06T5/20 ,  G01N1/30

CPC分类号： G06K9/00127

摘要： A convolutional neural network predicts which regions of a tissue slice would be stained by a first stain by training a model to identify those regions based only on tissue stained by a second stain. Thereafter the first stain need not be used to mark cancerous regions on other tissue slices that are stained with the second stain. The training slice is stained with a first immunohistochemical stain and a second counterstain. A target region of an image of the training slice is identified using image analysis based on the first stain. A set of parameters for associated mathematical operations are optimized to train the model to classify pixels of the image as belonging to the target region based on the second stain but not on the first stain. The trained parameters are stored in a database and applied to other images of tissue not stained with the first stain.

公开(公告)号：US20160106836A1

公开(公告)日：2016-04-21

申请号：US14882862

申请日：2015-10-14

申请人： Definiens AG

发明人： Gerd Binnig ,  Guenter Schmidt

IPC分类号： A61K39/395 ,  G01N33/50 ,  A61K45/06

CPC分类号： A61K39/39558 ,  A61K39/3955 ,  A61K45/06 ,  A61K2039/507 ,  C07K16/2818 ,  C07K16/32 ,  G01N33/5011 ,  G01N2500/10 ,  G01N2800/52 ,  G01N2800/7028

摘要： A method for treating a malignant tumor in a patient identifies tumor cells using a logical AND operation on antigens on the surfaces of the tumor cells. First and second antigens are determined to be present on the tumor cells. A first medication including a first antibody and a second antibody is administered to the patient. The first antibody is linked to a first dock, and the second antibody is linked to a second dock. In the patient's body, the first antibody binds to a first antigen, and the second antibody binds to a second antigen. After the elapse of a first predetermined interval, a second medication is administered to the patient. The second medication forms a structured binding site when the second medication simultaneously binds to both the first dock and the second dock. After the elapse of a second predetermined interval, a third medication is administered to the patient. The third medication binds only to the structured binding site and activates immune cells of the patient.

摘要翻译： 用于治疗患者恶性肿瘤的方法使用对肿瘤细胞表面的抗原的逻辑AND操作鉴定肿瘤细胞。 确定第一和第二抗原存在于肿瘤细胞上。 向患者施用包括第一抗体和第二抗体的第一药物。 第一抗体连接到第一个基因座，第二个抗体连接到第二个基因座。 在患者体内，第一抗体与第一抗原结合，第二抗体与第二抗原结合。 在经过第一预定间隔之后，向患者施用第二药物。 第二药物当第二药物同时结合第一码头和第二码头时形成结构化的结合位点。 在经过第二预定间隔之后，向患者施用第三药物。 第三种药物仅结合​​结构结合位点并激活患者的免疫细胞。

公开(公告)号：US20130208950A1

公开(公告)日：2013-08-15

申请号：US13776853

申请日：2013-02-26

申请人： Definiens AG

发明人： Maria Athelogou ,  Gerd Binning ,  Guenter Schmidt ,  Tamara Manuelian ,  Joachim Diebold

IPC分类号： G06T7/00

CPC分类号： G06T7/0012 ,  G06K9/00134 ,  G06T2207/10056 ,  G06T2207/10064 ,  G06T2207/30024

摘要： An analysis system automatically analyzes and counts fluorescence signals present in biopsy tissue marked using Fluorescence in situ Hybridization (FISH). The user of the system specifies classes of a class network and process steps of a process hierarchy. Then pixel values in image slices of biopsy tissue are acquired in three dimensions. A computer-implemented network structure is generated by linking pixel values to objects of a data network according to the class network and process hierarchy. Objects associated with pixel values at different depths of the biopsy tissue are used to determine the number, volume and distance between cell components. In one application, fluorescence signals that mark Her2/neural genes and centromeres of chromosome seventeen are counted to diagnose breast cancer. Her2/neural genes that overlap one another or that are covered by centromeres can be accurately counted. Signal artifacts that do not mark genes can be identified by their excessive volume.

摘要翻译： 分析系统自动分析和计数使用荧光原位杂交（FISH）标记的活检组织中存在的荧光信号。 系统的用户指定类网络的类和进程层次结构的处理步骤。 然后在三维中获取活检组织的图像切片中的像素值。 通过根据类网络和进程层次将像素值链接到数据网络的对象来生成计算机实现的网络结构。 与活检组织不同深度处的像素值相关联的物体用于确定细胞组分之间的数量，体积和距离。 在一个应用中，计数标记Her2 /神经基因的荧光信号和17号染色​​体的着丝粒被诊断为乳腺癌。 可以准确计算Her2 /彼此重叠或被着丝粒覆盖的神经基因。 不标记基因的信号伪影可以通过其过量的体积来识别。

公开(公告)号：US09740957B2

公开(公告)日：2017-08-22

申请号：US14473096

申请日：2014-08-29

申请人： Definiens AG

发明人： Olivier Pauly ,  Nicolas Brieu ,  Guenter Schmidt ,  Johannes Zimmermann ,  Gerd Binnig

IPC分类号： G06K7/00 ,  G06K9/46 ,  G06K9/00 ,  G06T7/00 ,  G06K9/62

CPC分类号： G06K9/0014 ,  G06K9/4628 ,  G06K9/4642 ,  G06K9/4652 ,  G06K9/6209 ,  G06K9/6212 ,  G06K9/6282 ,  G06T7/0012

摘要： 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.

公开(公告)号：US20170193660A1

公开(公告)日：2017-07-06

申请号：US15443672

申请日：2017-02-27

申请人： Definiens AG

发明人： Guenter Schmidt ,  Arno Schaepe ,  Thomas Heydler ,  Markus Rinecker ,  Gerd Binnig

IPC分类号： G06T7/00 ,  A61B6/00 ,  G06K9/62 ,  G06F19/00 ,  G06T7/40

CPC分类号： G06T7/0012 ,  A61B6/502 ,  A61B6/5217 ,  G06F19/321 ,  G06K9/6201 ,  G06Q10/10 ,  G06Q50/22 ,  G06T7/40 ,  G06T2207/10116 ,  G06T2207/30068 ,  G06T2207/30096 ,  G16H50/20 ,  G16H50/70

摘要： A clinical decision support system performs a similarity search to determine the probable outcome of applying on a current patient those clinical actions that were performed on similar patients. The system analyzes stored electronic health records of similar patients so as to recommend diagnostic and therapeutic steps for the current patient. The system receives the health record of the patient, determines which clinical actions were already applied on the patient, generates classifiers associated with potential future clinical actions, generates a success value for each health record of another patient using the classifiers, displays the health record of the other patient having the greatest success value, and indicates a proposed clinical action that is to be applied on the patient. The system also calculates a quality value indicating the probability that a sequence of clinical actions that were applied to a similar patient will be successful if applied to the patient.

公开(公告)号：US09159129B2

公开(公告)日：2015-10-13

申请号：US14197197

申请日：2014-03-04

申请人： Definiens AG

发明人： Ralf Schoenmeyer ,  Gerd Binnig ,  Guenter Schmidt

IPC分类号： G06K9/00 ,  G06T7/00 ,  G06T11/20

CPC分类号： G06T7/0012 ,  G06T11/206 ,  G06T2207/10061 ,  G06T2207/20016 ,  G06T2207/20021 ,  G06T2207/20221

摘要： 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.

摘要翻译： 用于生成基于图像的测试的方法通过迭代地修改控制核心图像块的图像和数据分析的规则集来提高诊断精度。 染色的组织切片的数字图像被分割成瓦片，并且来自不同图像的瓦片是核心的。 第一个图像对象链接到图块的所选像素。 通过测量第一个对象来生成第一个数字数据。 热图的每个像素聚集来自核心层的瓦片的第一数值数据。 第二个对象与热图的选定像素相关联。 测量第二个物体产生第二数值数据。 该方法改善了第二数值数据与通过修改用于生成第一和第二对象的规则集以及第一和第二数值数据来分析组织的患者的临床数据相关的程度。 测试由与临床数据产生最佳相关性的那些规则集定义。

公开(公告)号：US20150161484A1

公开(公告)日：2015-06-11

申请号：US14623227

申请日：2015-02-16

申请人： Definiens AG

发明人： Gerd Binnig ,  Guenter Schmidt

IPC分类号： G06K9/62

CPC分类号： G06T7/0012 ,  G06K9/6268 ,  G06T2200/24 ,  G06T2207/20108 ,  G06T2207/30004

摘要： 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.

摘要翻译： 用于计算机辅助检测的系统使用计算机实现的网络结构来分析存在于人体的数字图像切片中的图案并且生成患者的三维解剖模型。 首先通过检测容易识别的器官，然后使用那些器官作为上下文对象来检测其他器官来产生解剖模型。 用户指定隶属函数，其定义网络结构的哪些对象属于在类层次结构中指定的各种人体器官。 潜在匹配类的隶属函数基于与候选对象链接的体素的属性与上下文对象的属性之间的关系确定网络结构的候选对象是否属于潜在类。 用于分类对象的一些体素属性是位置，亮度和体积。 然后测量人体器官以辅助患者的诊断。

公开(公告)号：US20170372118A1

公开(公告)日：2017-12-28

申请号：US15674695

申请日：2017-08-11

申请人： Definiens AG

发明人： Ralf Schoenmeyer ,  Gerd Binnig ,  Guenter Schmidt ,  Maria Athelogou ,  Peter Ellenberg

IPC分类号： G06K9/00 ,  G06T7/33

CPC分类号： G06K9/0014 ,  G06T7/33 ,  G06T2207/10056 ,  G06T2207/30024

摘要： 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.

公开(公告)号：US20150228075A1

公开(公告)日：2015-08-13

申请号：US14697378

申请日：2015-04-27

申请人： Definiens AG

发明人： Maria Athelogou ,  Gerd Binnig ,  Guenter Schmidt

IPC分类号： G06T7/00 ,  G06K9/46 ,  G06K9/62 ,  G06T7/40

CPC分类号： G06T7/0012 ,  G06K9/0014 ,  G06K9/34 ,  G06K9/4652 ,  G06K9/6215 ,  G06T7/11 ,  G06T7/155 ,  G06T7/187 ,  G06T7/90 ,  G06T2207/10024 ,  G06T2207/10056 ,  G06T2207/30024

摘要： 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.

摘要翻译： 用于确定测试生物标志物是否是细胞成分（例如膜或细胞核）类型的污渍的方法包括对用测试生物标志物染色的组织的图像执行各种分割过程。 一个分割过程搜索第一个单元分量类型，另一个分割过程通过仅分割染色的像素来搜索第二单元分量类型。 如果过程仅基于染色的像素识别组分，则将测试生物标志物鉴定为每种组分类型的污点。 测试生物标志物是膜污点还是核染色体显示在图形用户界面上。 此外，该方法使用被确定为对应于第一单元组件的像素来识别与第二单元组件对应的染色像素。 然后显示用于测试生物标志物的表达谱，其指示每种类型细胞组分中染色像素的比例。