Patents by Inventor Sami Petri PERTTU
Sami Petri PERTTU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240005515Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.Type: ApplicationFiled: September 14, 2023Publication date: January 4, 2024Inventors: Hannu Mikael LAAKSONEN, Janne NORD, Maria Isabel CORDERO MARCOS, Sami Petri PERTTU, Tomi RUOKOLA
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Patent number: 11842498Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.Type: GrantFiled: December 16, 2019Date of Patent: December 12, 2023Assignee: SIEMENS HEALTHINEERS INTERNATIONAL AGInventors: Hannu Mikael Laaksonen, Janne Nord, Maria Isabel Cordero Marcos, Sami Petri Perttu, Tomi Ruokola
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Patent number: 11776172Abstract: Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.Type: GrantFiled: April 14, 2022Date of Patent: October 3, 2023Inventors: Pascal Paysan, Benjamin M Haas, Janne Nord, Sami Petri Perttu, Dieter Seghers, Joakim Pyyry
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Publication number: 20230274817Abstract: Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.Type: ApplicationFiled: May 7, 2023Publication date: August 31, 2023Applicant: SIEMENS HEALTHINEERS INTERNATIONAL AGInventors: Hannu LAAKSONEN, Janne NORD, Sami Petri PERTTU
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Patent number: 11682485Abstract: Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.Type: GrantFiled: September 27, 2022Date of Patent: June 20, 2023Assignee: SIEMENS HEALTHINEERS INTERNATIONAL AGInventors: Hannu Laaksonen, Janne Nord, Sami Petri Perttu
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Publication number: 20230020911Abstract: Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.Type: ApplicationFiled: September 27, 2022Publication date: January 19, 2023Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AGInventors: Hannu LAAKSONEN, Janne NORD, Sami Petri PERTTU
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Patent number: 11475991Abstract: Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.Type: GrantFiled: September 28, 2018Date of Patent: October 18, 2022Inventors: Hannu Laaksonen, Janne Nord, Sami Petri Perttu
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Patent number: 11436766Abstract: Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data and processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating 2D feature data by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers.Type: GrantFiled: December 20, 2019Date of Patent: September 6, 2022Inventors: Pascal Paysan, Benjamin M Haas, Janne Nord, Sami Petri Perttu, Dieter Seghers, Joakim Pyyry
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Publication number: 20220245869Abstract: Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.Type: ApplicationFiled: April 14, 2022Publication date: August 4, 2022Applicant: Varian Medical Systems International AGInventors: Pascal PAYSAN, Benjamin M HAAS, Janne NORD, Sami Petri PERTTU, Dieter SEGHERS, Joakim PYYRY
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Patent number: 11386592Abstract: Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.Type: GrantFiled: December 20, 2019Date of Patent: July 12, 2022Inventors: Pascal Paysan, Benjamin M Haas, Janne Nord, Sami Petri Perttu, Dieter Seghers, Joakim Pyyry
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Patent number: 11282192Abstract: Example methods and systems for training deep learning engines for radiotherapy treatment planning are provided. One example method may comprise: obtaining a set of training data that includes unlabeled training data and labeled training data; and configuring a deep learning engine to include (a) a primary network and (b) a deep supervision network that branches off from the primary network. The method may further comprise: training the deep learning engine to perform the radiotherapy treatment planning task by processing training data instance to generate (a) primary output data and (b) deep supervision output data; and updating weight data associated with at least some of the multiple processing layers based on the primary output data and/or the deep supervision output data. The deep supervision network may be pruned prior to applying the primary network to perform the radiotherapy treatment planning task for a patient.Type: GrantFiled: December 19, 2019Date of Patent: March 22, 2022Inventors: Hannu Mikael Laaksonen, Janne Nord, Sami Petri Perttu
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Publication number: 20220051781Abstract: Example methods and systems for deep transfer learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining (310) a base deep learning engine that is pre-trained to perform a base radiotherapy treatment planning task; and based on the base deep learning engine, generating a target deep learning engine to perform a target radiotherapy treatment planning task. The target deep learning engine may be generated by configuring (330) a variable base layer among multiple base layers of the base deep learning engine, and generating (340) one of multiple target layers of the target deep learning engine by modifying the variable base layer. Alternatively or additionally, the target deep learning engine may be generated by configuring (350) an invariable base layer among the multiple base layers, and generating (360) one of multiple target layers of the target deep learning engine based on feature data generated using the invariable base layer.Type: ApplicationFiled: May 30, 2019Publication date: February 17, 2022Applicant: VARIAN MEDICAL SYSTEMS INTERNATIONAL AGInventors: Hannu Mikael LAAKSONEN, Sami Petri PERTTU, Tomi RUOKOLA, Jan SCHREIER, Janne NORD
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Publication number: 20210192810Abstract: Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.Type: ApplicationFiled: December 20, 2019Publication date: June 24, 2021Applicant: Varian Medical Systems International AGInventors: Pascal PAYSAN, Benjamin M HAAS, Janne NORD, Sami Petri PERTTU, Dieter SEGHERS, Joakim PYYRY
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Publication number: 20210192719Abstract: Example methods and systems for training deep learning engines for radiotherapy treatment planning are provided. One example method may comprise: obtaining a set of training data that includes unlabeled training data and labeled training data; and configuring a deep learning engine to include (a) a primary network and (b) a deep supervision network that branches off from the primary network. The method may further comprise: training the deep learning engine to perform the radiotherapy treatment planning task by processing training data instance to generate (a) primary output data and (b) deep supervision output data; and updating weight data associated with at least some of the multiple processing layers based on the primary output data and/or the deep supervision output data. The deep supervision network may be pruned prior to applying the primary network to perform the radiotherapy treatment planning task for a patient.Type: ApplicationFiled: December 19, 2019Publication date: June 24, 2021Applicant: Varian Medical Systems International AGInventors: Hannu Mikael LAAKSONEN, Janne NORD, Sami Petri PERTTU
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Publication number: 20210192809Abstract: Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data and processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating 2D feature data by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers.Type: ApplicationFiled: December 20, 2019Publication date: June 24, 2021Applicant: Varian Medical Systems International AGInventors: Pascal PAYSAN, Benjamin M HAAS, Janne NORD, Sami Petri PERTTU, Dieter SEGHERS, Joakim PYYRY
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Publication number: 20210183070Abstract: Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.Type: ApplicationFiled: December 16, 2019Publication date: June 17, 2021Inventors: Hannu Mikael LAAKSONEN, Janne NORD, Maria Isabel CORDERO MARCOS, Sami Petri PERTTU, Tomi RUOKOLA
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Patent number: 11013936Abstract: Example methods and systems for generating dose estimation models for radiotherapy treatment planning are provided. One example method may comprise obtaining model configuration data that specifies multiple anatomical structures based on which dose estimation is performed by a dose estimation model. The method may also comprise obtaining training data that includes a first treatment plan associated with a first past patient and multiple second treatment plans associated with respective second past patients. The method may further comprise: in response to determination that automatic segmentation is required for the first treatment plan, performing automatic segmentation on image data associated with the first past patient to generate an improved first treatment plan, and generating the dose estimation model based on the improved first treatment plan and the multiple second treatment plans.Type: GrantFiled: December 21, 2018Date of Patent: May 25, 2021Inventors: María Cordero Marcos, Esa Kuusela, Hannu Laaksonen, Sami Petri Perttu
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Patent number: 10984902Abstract: Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, transforming the treatment image data associated with the first imaging modality to generate transformed image data associated with the second imaging modality. The method may further comprise: processing, using the deep learning engine, the transformed image data to generate output data for updating the treatment plan.Type: GrantFiled: September 28, 2018Date of Patent: April 20, 2021Inventors: Hannu Laaksonen, Janne Nord, Sami Petri Perttu
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Publication number: 20200197726Abstract: Example methods and systems for generating dose estimation models for radiotherapy treatment planning are provided. One example method may comprise obtaining model configuration data that specifies multiple anatomical structures based on which dose estimation is performed by a dose estimation model. The method may also comprise obtaining training data that includes a first treatment plan associated with a first past patient and multiple second treatment plans associated with respective second past patients. The method may further comprise: in response to determination that automatic segmentation is required for the first treatment plan, performing automatic segmentation on image data associated with the first past patient to generate an improved first treatment plan, and generating the dose estimation model based on the improved first treatment plan and the multiple second treatment plans.Type: ApplicationFiled: December 21, 2018Publication date: June 25, 2020Applicant: Varian Medical Systems International AGInventors: María CORDERO MARCOS, Esa KUUSELA, Hannu LAAKSONEN, Sami Petri PERTTU
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Publication number: 20200105399Abstract: Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.Type: ApplicationFiled: September 28, 2018Publication date: April 2, 2020Applicant: Varian Medical Systems International AGInventors: Hannu LAAKSONEN, Janne NORD, Sami Petri PERTTU