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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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3.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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公开(公告)号：US20210334974A1
公开(公告)日：2021-10-28
申请号：US17008404
申请日：2020-08-31
Applicant: EXINI Diagnostics AB
Inventor： Kerstin Elsa Maria Johnsson , Johan Martin Brynolfsson , Hannicka Maria Eleonora Sahlstedt
Abstract: Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection/risk assessment within the identified region of interest).
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公开(公告)号：US20250061580A1
公开(公告)日：2025-02-20
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: G06T7/11 , 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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Patent Agency Ranking
6.发明公开公开(公告)号：US20230420112A1
公开(公告)日：2023-12-28
申请号：US18209676
申请日：2023-06-14
Applicant: EXINI Diagnostics AB
Inventor： Johan Martin Brynolfsson , Kerstin Elsa Maria Johnsson , Hannicka Maria Eleonora Sahlstedt
CPC classification number: G16H30/40 , G16H50/20 , G16H50/30 , G06T7/11 , G06T7/0012 , G06T2207/10072 , G06T2207/30096 , G06T2207/30056
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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公开(公告)号：US11386988B2
公开(公告)日：2022-07-12
申请号：US17020161
申请日：2020-09-14
Applicant: EXINI Diagnostics AB
Inventor： Kerstin Elsa Maria Johnsson , Johan Martin Brynolfsson , Hannicka Maria Eleonora Sahlstedt
Abstract: Presented herein are systems and methods that provide for improved 3D segmentation of nuclear medicine images using an artificial intelligence-based deep learning approach. For example, in certain embodiments, the machine learning module receives both an anatomical image (e.g., a CT image) and a functional image (e.g., a PET or SPECT image) as input, and generates, as output, a segmentation mask that identifies one or more particular target tissue regions of interest. The two images are interpreted by the machine learning module as separate channels representative of the same volume. Following segmentation, additional analysis can be performed (e.g., hotspot detection/risk assessment within the identified region of interest).
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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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