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公开(公告)号：US20240296949A1
公开(公告)日：2024-09-05
申请号：US18503002
申请日：2023-11-06
Applicant: VisionGate, Inc.
Inventor： Michael G. Meyer , David Wilbur , Chris Presley , Celeste Hamilton , Rahul Katdare , Greg Zick , Frances Lakers
IPC: G16H50/20 , G01N15/14 , G01N15/1434 , G01N33/483 , G16H10/60 , G16H30/40
CPC classification number: G16H50/20 , G01N15/1434 , G01N33/4833 , G16H10/60 , G16H30/40 , G01N2015/1402 , G01N2015/1445
Abstract: A cell classification system includes an optical tomography system and a processor operable to generate a plurality of 2D images and a plurality of pseudo-projection images of a cell. The processor executes instructions that cause the processor to: generate a 3D image of the cell using the pseudo-projection images; apply digital enhancement to a 2D or 3D image to improve determination of boundaries of structures with the cell that provide indicia of cell features; analyze at least one of the cell features or 2D or 3D images using AI-based cell characterization to characterize the cell as normal or as having abnormal features by analyzing the boundaries of structures; create a Normal Cell Gallery comprising images characterized by AI-based characterization as normal; create a Diagnostic Cell Gallery with images characterized by AI-based characterization as having abnormal features; display at least the Diagnostic Cell Gallery; and record a user classification.
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公开(公告)号：US11545237B2
公开(公告)日：2023-01-03
申请号：US16650304
申请日：2018-09-26
Applicant: VISIONGATE, INC.
Inventor： Michael G. Meyer , Daniel J. Sussman , Rahul Katdare , Laimonas Kelbauskas , Alan C. Nelson , Randall Mastrangelo
IPC: G06T7/00 , G16B40/20 , G06T7/194 , G06T7/11 , G01N15/14 , G06K9/62 , G06V10/40 , G06V20/64 , G06V20/69 , G01N15/10
Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.
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公开(公告)号：US20210200987A1
公开(公告)日：2021-07-01
申请号：US16972552
申请日：2019-06-05
Applicant: VISIONGATE, INC.
Inventor： Daniel J. Sussman , Michael G. Meyer , Randall Mastrangelo , Alan C. Nelson
Abstract: A method to develop one or more morphometric classifiers to identify a mismatch repair deficiency (MMRD). The method provides a non-invasive method of characterizing MMRD that is responsive to a tumor in its early stages of development and irrespective of the tumor size. The method allows targeting cancer therapy to the specific characteristics of the cancer that the patient may have, allowing more efficient cancer management with far fewer side effects.
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公开(公告)号：US11885732B2
公开(公告)日：2024-01-30
申请号：US17968668
申请日：2022-10-18
Applicant: VisionGate, Inc.
Inventor： Michael G. Meyer , Daniel J. Sussman , Rahul Katdare , Laimonas Kelbauskas , Alan C. Nelson , Randall Mastrangelo
IPC: G06T7/00 , G01N15/14 , G06T7/194 , G06T7/11 , G16B40/20 , G06V20/64 , G06V20/69 , G06F18/214 , G06V10/40 , G01N15/10
CPC classification number: G01N15/1434 , G01N15/147 , G01N15/1429 , G01N15/1475 , G06F18/214 , G06T7/0012 , G06T7/11 , G06T7/194 , G06V10/40 , G06V20/64 , G06V20/695 , G06V20/698 , G16B40/20 , G01N2015/1006 , G01N2015/1445 , G06T2207/10101 , G06T2207/20081 , G06T2207/30024 , G06V2201/03
Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.
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公开(公告)号：US11069054B2
公开(公告)日：2021-07-20
申请号：US15403940
申请日：2017-01-11
Applicant: VISIONGATE, INC.
Inventor： Alan C. Nelson , Michael G. Meyer , Daniel J. Sussman
IPC: A61K31/167 , G06T7/00 , G01N33/483 , A61K31/5578 , A61K31/593 , G01N15/14 , G06K9/00 , G01N15/10
Abstract: A method of treating a malignancy in a human subject by analyzing pseudo-projection images of cells obtained from a sputum specimen obtained from a subject employs a biological specimen classifier that identifies cells from the sputum specimen as normal or abnormal. If abnormal cells are detected, then the abnormal cells are further classified as dysplastic or cancerous. If the cells are classified as dysplastic, then an immunomodulating agent is administered to the subject over a predetermined time period designed to achieve a therapeutic dosage.
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Patent Agency Ranking
公开(公告)号：US20210049425A1
公开(公告)日：2021-02-18
申请号：US16976015
申请日：2019-02-28
Applicant: VISIONGATE, INC.
Inventor： Michael G. Meyer , Laimonis Kelbauskas , Rahul Katdare , Daniel J. Sussman , Timothy Bell , Alan C. Nelson
Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.
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7.发明申请SYSTEM AND METHOD FOR DETERMINING CELL ADEQUACY IN A CYTOLOGICAL ANALYSIS SYSTEM 有权用于确定细胞分析系统中细胞增殖的系统和方法公开(公告)号：US20170003267A1
公开(公告)日：2017-01-05
申请号：US14788015
申请日：2015-06-30
Applicant: VISIONGATE, INC.
Inventor： Michael G. Meyer , Rahul Katdare , Chris Presley , Timothy Bell
IPC: G01N33/483
CPC classification number: G01N33/4833 , G01N15/14 , G01N15/1434 , G01N15/147 , G01N15/1475 , G01N2015/1006 , G01N2015/1402 , G01N2015/1445 , G01N2015/1486 , G06T11/006 , G06T2207/20081
Abstract: A cytological analysis test for 3D cell classification from a specimen. The method includes isolating and preserving a cell from the specimen and enriching the cell before embedding the enriched cell into an optical medium. The embedded cell is injected into a capillary tube where pressure is applied until the cell appears in a field of view of a pseudo-projection viewing subsystem to acquire a pseudo-projection image. The capillary tube rotates about a tube axis to provide a set of pseudo-projection images for each embedded cell which are reconstructed to produce a set of 3D cell reconstructions. Reference cells are classified and enumerated and a second cell classifier detects target cells. An adequacy classifier compares the number of reference cells against a threshold value of enumerated reference cells to determine specimen adequacy.
Abstract translation: 来自标本的3D细胞分类的细胞学分析测试。 该方法包括从样品中分离和保存细胞并在将富集的细胞嵌入光学介质之前富集细胞。 将嵌入式电池注入毛细管中，其中施加压力，直到细胞出现在伪投影观察子系统的视场中以获得伪投影图像。 毛细管围绕管轴旋转，以为每个嵌入式单元提供一组伪投影图像，这些图像被重建以产生一组3D细胞重建。 参考细胞被分类和枚举，第二细胞分类器检测靶细胞。 适当的分类器将参考单元的数量与枚举的参考单元的阈值进行比较，以确定样本充足度。
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公开(公告)号：US12019008B2
公开(公告)日：2024-06-25
申请号：US18064759
申请日：2022-12-12
Applicant: VISIONGATE, INC.
Inventor： Michael G. Meyer , Laimonas Kelbauskas , Rahul Katdare , Daniel J. Sussman , Timothy Bell , Alan C. Nelson
IPC: G01N15/1433 , G01N15/14 , G01N15/1434 , G01N33/483 , G06F18/214 , G06F18/243 , G06T7/00 , G06T11/00 , G06V10/40 , G06V10/762 , G06V10/774
CPC classification number: G01N15/1433 , G01N15/147 , G01N33/4833 , G06F18/2148 , G06F18/24317 , G06T7/0012 , G06T11/003 , G06V10/40 , G06V10/762 , G06V10/7747 , G01N2015/1445 , G06T2207/10101 , G06T2207/30024 , G06T2207/30096 , G06V2201/03
Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.
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公开(公告)号：US20230289407A1
公开(公告)日：2023-09-14
申请号：US18064759
申请日：2022-12-12
Applicant: VISIONGATE, INC.
Inventor： Michael G. Meyer , Laimonas Kelbauskas , Rahul Katdare , Daniel J. Sussman , Timothy Bell , Alan C. Nelson
CPC classification number: G06K9/6281 , G01N15/147 , G01N15/1475 , G01N33/4833 , G06K9/6257 , G06T7/0012 , G06T11/003 , G06V10/40 , G01N2015/1445 , G06T2207/10101 , G06T2207/30024 , G06T2207/30096 , G06V2201/03
Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.
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10.发明授权System and method for determining cell adequacy in a cytological analysis system 有权用于确定细胞学分析系统中细胞充足性的系统和方法公开(公告)号：US09594072B2
公开(公告)日：2017-03-14
申请号：US14788015
申请日：2015-06-30
Applicant: VISIONGATE, INC.
Inventor： Michael G. Meyer , Rahul Katdare , Chris Presley , Timothy Bell
IPC: G01N33/483
CPC classification number: G01N33/4833 , G01N15/14 , G01N15/1434 , G01N15/147 , G01N15/1475 , G01N2015/1006 , G01N2015/1402 , G01N2015/1445 , G01N2015/1486 , G06T11/006 , G06T2207/20081
Abstract: A cytological analysis test for 3D cell classification from a specimen. The method includes isolating and preserving a cell from the specimen and enriching the cell before embedding the enriched cell into an optical medium. The embedded cell is injected into a capillary tube where pressure is applied until the cell appears in a field of view of a pseudo-projection viewing subsystem to acquire a pseudo-projection image. The capillary tube rotates about a tube axis to provide a set of pseudo-projection images for each embedded cell which are reconstructed to produce a set of 3D cell reconstructions. Reference cells are classified and enumerated and a second cell classifier detects target cells. An adequacy classifier compares the number of reference cells against a threshold value of enumerated reference cells to determine specimen adequacy.
Abstract translation: 来自标本的3D细胞分类的细胞学分析测试。 该方法包括从样品中分离和保存细胞并在将富集的细胞嵌入光学介质之前富集细胞。 将嵌入式电池注入毛细管中，其中施加压力，直到细胞出现在伪投影观察子系统的视场中以获得伪投影图像。 毛细管围绕管轴旋转，以为每个嵌入式单元提供一组伪投影图像，这些图像被重建以产生一组3D细胞重建。 参考细胞被分类和枚举，第二细胞分类器检测靶细胞。 适当的分类器将参考单元的数量与枚举的参考单元的阈值进行比较，以确定样本充足度。
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