Patents Assigned to EXINI DIAGNOSTICS AB
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Patent number: 11937962Abstract: 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.Type: GrantFiled: November 18, 2022Date of Patent: March 26, 2024Assignees: Progenics Pharmaceuticals, Inc., EXINI Diagnostics ABInventors: Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter, Lars Edenbrandt
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Patent number: 11941817Abstract: 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.Type: GrantFiled: March 29, 2023Date of Patent: March 26, 2024Assignee: EXINI Diagnostics ABInventors: Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson, Aseem Undvall Anand
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Patent number: 11900597Abstract: 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.Type: GrantFiled: August 21, 2020Date of Patent: February 13, 2024Assignees: Progenics Pharmaceuticals, Inc., EXINI Diagnostics ABInventors: Aseem Undvall Anand, Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter
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Patent number: 11721428Abstract: 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.Type: GrantFiled: August 31, 2020Date of Patent: August 8, 2023Assignee: EXINI Diagnostics ABInventors: Johan Martin Brynolfsson, Kerstin Elsa Maria Johnsson, Hannicka Maria Eleonora Sahlstedt
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Patent number: 11657508Abstract: 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.Type: GrantFiled: January 6, 2020Date of Patent: May 23, 2023Assignee: EXINI Diagnostics ABInventors: Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson, Aseem Undvall Anand
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Patent number: 11564621Abstract: 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.Type: GrantFiled: January 6, 2020Date of Patent: January 31, 2023Assignees: Progenies Pharmacenticals, Inc., EXINI Diagnostics ABInventors: Aseem Undvall Anand, Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter
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Patent number: 11534125Abstract: 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.Type: GrantFiled: April 23, 2020Date of Patent: December 27, 2022Assignees: Progenies Pharmaceuticals, Inc., EXINI Diagnostics ABInventors: Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter, Lars Edenbrandt
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Patent number: 11386988Abstract: 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).Type: GrantFiled: September 14, 2020Date of Patent: July 12, 2022Assignee: EXINI Diagnostics ABInventors: Kerstin Elsa Maria Johnsson, Johan Martin Brynolfsson, Hannicka Maria Eleonora Sahlstedt
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Patent number: 11321844Abstract: 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).Type: GrantFiled: August 31, 2020Date of Patent: May 3, 2022Assignee: EXINI Diagnostics ABInventors: Kerstin Elsa Maria Johnsson, Johan Martin Brynolfsson, Hannicka Maria Eleonora Sahlstedt
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Patent number: 10973486Abstract: 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.Type: GrantFiled: June 7, 2018Date of Patent: April 13, 2021Assignees: Progenics Pharmaceuticals, Inc., EXINI Diagnostics ABInventors: Karl Vilhelm Sjöstrand, Jens Filip Andreas Richter, Kerstin Elsa Maria Johnsson, Erik Konrad Gjertsson
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Patent number: 8855387Abstract: The invention relates to a detection system for automatic detection of bone cancer metastases from a set of isotope bone scan images of a patients skeleton, the system comprising a shape identifier unit, a hotspot detection unit, a hotspot feature extraction unit, a first artificial neural network unit, a patient feature extraction unit, and a second artificial neural network unit.Type: GrantFiled: December 23, 2008Date of Patent: October 7, 2014Assignee: Exini Diagnostics ABInventors: Iman Hamadeh, Pierre Nordblom, Karl Sjöstrand
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Publication number: 20130094704Abstract: The invention relates to a detection system for automatic detection of bone cancer metastases from a set of isotope bone scan images of a patients skeleton, the system comprising a shape identifier unit, a hotspot detection unit, a hotspot feature extraction unit, a first artificial neural network unit, a patient feature extraction unit, and a second artificial neural network unit.Type: ApplicationFiled: December 23, 2008Publication date: April 18, 2013Applicant: EXINI DIAGNOSTICS ABInventors: Iman Hamadeh, Pierre Nordblom, Karl Sjöstrand
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Publication number: 20100067761Abstract: Methods for fully automatic quantification and interpretation of three dimensional images of the brain or other organs. A system for Computer Aided Diagnosis (CAD) of diseases affecting cerebral cortex from SPECT images of the brain, where said images may represent cerebral blood flow (CBF). The methods include image processing, statistical shape models, a virtual brain atlas, reference databases and machine learning.Type: ApplicationFiled: March 26, 2007Publication date: March 18, 2010Applicant: EXINI DIAGNOSTICS ABInventors: David JAKOBSSON, Jens RICHTER, Andreas JARUND
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Patent number: RE47609Abstract: The invention relates to a detection system for automatic detection of bone cancer metastases from a set of isotope bone scan images of a patients skeleton, the system comprising a shape identifier unit, a hotspot detection unit, a hotspot feature extraction unit, a first artificial neural network unit, a patient feature extraction unit, and a second artificial neural network unit.Type: GrantFiled: September 30, 2016Date of Patent: September 17, 2019Assignee: Exini Diagnostics ABInventors: Iman Hamadeh, Pierre Nordblom, Karl Sjöstrand