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公开(公告)号：US10973486B2
公开(公告)日：2021-04-13
申请号：US16003006
申请日：2018-06-07
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
Inventor： Karl Vilhelm Sjöstrand , Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson
IPC: G06K9/46 , A61B6/00 , G06T7/00 , G06T5/30 , A61B6/03 , A61K51/04 , G06T15/08 , G06T7/143 , G06T7/11 , G06N3/08 , G06F3/0484 , G06N3/04
Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific organs and/or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.
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2.发明申请公开(公告)号：US20190209116A1
公开(公告)日：2019-07-11
申请号：US16003006
申请日：2018-06-07
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
Inventor： Karl Vilhelm Sjöstrand , Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson
CPC classification number: A61B6/5217 , A61B6/037 , A61B6/465 , A61B6/466 , A61B6/467 , A61B6/50 , A61B6/5223 , A61B6/5229 , A61B6/5258 , A61K51/0478 , G06F3/04842 , G06K9/4647 , G06K2209/051 , G06N3/04 , G06N3/0454 , G06N3/08 , G06T5/30 , G06T7/0014 , G06T15/08 , G06T2200/24 , G06T2207/10108 , G06T2207/30081 , G06T2207/30096
Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific organs and/or tissue. In certain embodiments, the accurate identification of one or more such volumes can be used to determine quantitative metrics that measure uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.
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公开(公告)号：US11937962B2
公开(公告)日：2024-03-26
申请号：US17989863
申请日：2022-11-18
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
Inventor： Karl Vilhelm Sjöstrand , Jens Filip Andreas Richter , Lars Edenbrandt
IPC: G06K9/00 , A61B6/00 , A61B6/03 , A61K51/04 , G06T7/00 , G06T7/11 , G06T7/73 , G16H15/00 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/70 , G16H70/60
CPC classification number: A61B6/469 , A61B6/037 , A61B6/465 , A61B6/505 , A61B6/5217 , A61B6/5258 , A61B6/563 , A61K51/0489 , G06T7/0012 , G06T7/11 , G06T7/73 , G16H15/00 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/70 , G16H70/60 , G06T2200/24 , G06T2207/30008 , G06T2207/30096
Abstract: Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.
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公开(公告)号：US20230410985A1
公开(公告)日：2023-12-21
申请号：US18207246
申请日：2023-06-08
Applicant: EXINI Diagnostics AB , Progenics Pharmaceuticals, Inc.
Inventor： Johan Martin Brynolfsson , Hannicka Maria Eleonora Sahlstedt , Jens Filip Andreas Richter , Karl Vilhelm Sjöstrand , Aseem Undvall Anand
CPC classification number: G16H30/40 , G16H50/30 , G06T7/11 , G06T2207/10028 , G06T2207/30096
Abstract: Presented herein are systems and methods that provide semi-automated and/or automated analysis of medical image data to determine and/or convey values of metrics that provide a picture of a patient's risk and/or disease. Technologies described herein include systems and methods for analyzing medical image data to evaluate quantitative metrics that provide snapshots of patient disease burden at particular times and/or for analyzing images taken over time to produce a longitudinal dataset that provides a picture of how a patient's risk and/or disease evolves over time during surveillance and/or in response to treatment. Metrics computed via image analysis tools described herein may themselves be used as quantitative measures of disease burden and/or may be linked to clinical endpoints that seek to measure and/or stratify patient outcomes. Accordingly, image analysis technologies of the present disclosure may be used to inform clinical decision making, evaluate of treatment efficacy, and predict patient response(s).
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5.发明公开公开(公告)号：US20230351586A1
公开(公告)日：2023-11-02
申请号：US18014214
申请日：2021-07-02
Applicant: EXINI Diagnostics AB
Inventor： Johan Martin Brynolfsson , Kerstin Elsa Maria Johnsson , Hannicka Maria Eleonora Sahlstedt , Jens Filip Andreas Richter
CPC classification number: G06T7/0012 , G06T7/10 , G06T15/00 , G06V10/25 , G06V10/764 , G16H50/30 , G06T2207/10072 , G06T2207/30056 , G06T2207/30084 , G06T2207/30096 , G06V2201/07
Abstract: Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and/or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20200337658A1
公开(公告)日：2020-10-29
申请号：US16856120
申请日：2020-04-23
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
Inventor： Karl Vilhelm Sjöstrand , Jens Filip Andreas Richter , Lars Edenbrandt
IPC: A61B6/00 , A61B6/03 , A61K51/04 , G06T7/11 , G06T7/73 , G06T7/00 , G16H30/40 , G16H30/20 , G16H15/00 , G16H70/60 , G16H50/70 , G16H50/20 , G16H50/30
Abstract: Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.
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公开(公告)号：US12243236B1
公开(公告)日：2025-03-04
申请号：US18934154
申请日：2024-10-31
Applicant: EXINI Diagnostics AB
Inventor： Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson , Aseem Undvall Anand
IPC: G06T7/00 , A61B6/00 , A61B6/03 , A61B6/46 , A61B6/50 , A61K51/04 , G06F18/214 , G06T7/11 , G06V20/64 , G06V20/69 , G06V30/24 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50
Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.
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公开(公告)号：US20250069232A1
公开(公告)日：2025-02-27
申请号：US18934158
申请日：2024-10-31
Applicant: EXINI Diagnostics AB
Inventor： Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson , Aseem Undvall Anand
IPC: G06T7/11 , A61B6/00 , A61B6/03 , A61B6/46 , A61B6/50 , A61K51/04 , G06F18/214 , G06V20/64 , G06V20/69 , G06V30/24 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50
Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.
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公开(公告)号：US20240169546A1
公开(公告)日：2024-05-23
申请号：US18429322
申请日：2024-01-31
Applicant: EXINI Diagnostics AB
Inventor： Jens Filip Andreas Richter , Kerstin Elsa Maria Johnsson , Erik Konrad Gjertsson , Aseem Undvall Anand
IPC: G06T7/11 , A61B6/00 , A61B6/03 , A61B6/46 , A61B6/50 , A61K51/04 , G06F18/214 , G06V20/64 , G06V20/69 , G06V30/24 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50
CPC classification number: G06T7/11 , A61B6/032 , A61B6/037 , A61B6/463 , A61B6/466 , A61B6/481 , A61B6/505 , A61B6/507 , A61B6/5205 , A61B6/5241 , A61B6/5247 , A61K51/0455 , G06F18/214 , G06V20/64 , G06V20/695 , G06V20/698 , G06V30/2504 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50 , G06V2201/031 , G06V2201/033
Abstract: Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.
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10.发明公开公开(公告)号：US20240127437A1
公开(公告)日：2024-04-18
申请号：US18398846
申请日：2023-12-28
Applicant: Progenics Pharmaceuticals, Inc. , EXINI Diagnostics AB
CPC classification number: G06T7/0012 , G06V10/25 , G16H30/40 , G16H50/20 , G06T2207/10081
Abstract: Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and/or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and/or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and/or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.
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