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公开(公告)号：US11321844B2
公开(公告)日：2022-05-03
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：US20210335480A1
公开(公告)日：2021-10-28
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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