-
公开(公告)号：US11748981B2
公开(公告)日：2023-09-05
申请号：US17731228
申请日：2022-04-27
发明人： Guenter Schmidt , Nicolas Brieu , Ansh Kapil , Jan Martin Lesniak
IPC分类号： G06V10/82 , G06T7/00 , G06V20/69 , G06V10/764 , G06T7/136 , G06T7/33 , G06T7/35 , G01N1/30 , G06F18/2413
CPC分类号： G06V10/82 , G01N1/30 , G06F18/2414 , G06T7/0012 , G06T7/136 , G06T7/337 , G06T7/35 , G06V10/764 , G06V20/695 , G06V20/698 , G01N2800/52 , G01N2800/7028 , G06T2207/20084 , G06T2207/30024 , G06T2207/30242
摘要： A method for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold.
-
公开(公告)号：US11651863B2
公开(公告)日：2023-05-16
申请号：US16271827
申请日：2019-02-09
发明人： Guenter Schmidt
IPC分类号： G16H50/30 , G16B25/10 , G16B45/00 , G16B20/00 , G16B40/00 , G16H40/63 , G01N33/574 , G16H30/40 , G16H50/20 , G16B40/20 , C12Q1/6886
CPC分类号： G16H50/30 , C12Q1/6886 , G01N33/57434 , G16B20/00 , G16B25/10 , G16B40/00 , G16B40/20 , G16B45/00 , G16H30/40 , G16H40/63 , G16H50/20 , G01N2570/00 , G01N2800/342 , G01N2800/52 , G01N2800/54 , G01N2800/7028
摘要： A method that provides a graphical indication of whether a patient will have cancer recurrence uses univariate and bivariate prognostic features that were generated as part of a minimal spanning tree (MST). The method determines the values of first and second features. A first value is measured by detecting objects in an image of tissue from the cancer patient stained with a protein-specific IHC biomarker. A second value is measured using objects marked with an mRNA-specific probe biomarker detected in the tissue. The first feature is the univariate prognostic feature for cancer recurrence in a cohort of cancer patients. A combination of the first and second features is the bivariate prognostic feature for cancer recurrence in the cohort. The first and second features are elements of the MST. Nodes of the MST represent the univariate features, edges represent the bivariate features, and edge weights represent prognostic significance of bivariate features.
-
公开(公告)号：US20230177341A1
公开(公告)日：2023-06-08
申请号：US18105249
申请日：2023-02-03
CPC分类号： G06N3/084 , G06V20/69 , G06N20/10 , G01N1/30 , G06N5/04 , G06T5/20 , G06T7/0012 , G06T2207/20081 , G06T2207/30024
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：US11977984B2
公开(公告)日：2024-05-07
申请号：US18105249
申请日：2023-02-03
CPC分类号： G06N3/084 , G01N1/30 , G06N5/04 , G06N20/10 , G06T5/20 , G06T7/0012 , G06V20/69 , G06T2207/20081 , G06T2207/30024
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：US11593656B2
公开(公告)日：2023-02-28
申请号：US16221403
申请日：2018-12-14
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：US20220254020A1
公开(公告)日：2022-08-11
申请号：US17731228
申请日：2022-04-27
发明人： Guenter Schmidt , Nicolas Brieu , Ansh Kapil , Jan Martin Lesniak
摘要： A method for indicating how a cancer patient will respond to a predetermined therapy relies on spatial statistical analysis of classes of cell centers in a digital image of tissue of the cancer patient. The cell centers are detected in the image of stained tissue of the cancer patient. For each cell center, an image patch that includes the cell center is extracted from the image. A feature vector is generated based on each image patch using a convolutional neural network. A class is assigned to each cell center based on the feature vector associated with each cell center. A score is computed for the image of tissue by performing spatial statistical analysis based on classes of the cell centers. The score indicates how the cancer patient will respond to the predetermined therapy. The predetermined therapy is recommended to the patient if the score is larger than a predetermined threshold.
-
-
-
-
-
搜索结果
6 条记录 (耗时 0.013 秒)
