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US 12,224,067 B1Granted
Network for medical image analysis, decision support system, and related graphical user interface (GUI) applications
AssigneeProgenics Pharmaceuticals, Inc.
InventorMark R. Baker
Life of this patent
Estimating the term…
Abstract
Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and/or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and/or to track treatment and/or the progress of a disease.
Claims34
A network-based decision support system comprising:
- a processor;
- and
- a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:
- (i) receive and store a plurality of medical images in a database, each medical image associated with a corresponding patient;
- (ii) access one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprises a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient;
- (iii) automatically segment the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more region(s) of imaged tissue;
- and
- (iv) transfer the identified 3D boundaries to the PET scan, identify one or more hotspots within the PET scan, and compute, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue, one or more risk index values, each risk index value indicative of a cancer state or progression in the first patient, wherein the one or more risk index values comprises a first risk index value based on a total volume of identified hotspots within one or more of the identified 3D boundaries.
5 more elements
A method comprising:
- (i) receiving and storing, by a processor of a computing device, a plurality of medical images in a database, each medical image associated with a corresponding patient;
- (ii) accessing, by the processor, one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprises a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient;
- (iii) automatically segmenting, by the processor, the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more regions of imaged tissue;
- and
- (iv) transferring, by the processor, the identified 3D boundaries to the PET scan, identifying, by the processor, one or more hotspots within the PET scan, and computing, by the processor, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue, a value of each of one or more risk indices, each risk index value indicative of a cancer state or progression in the first patient, wherein the one or more risk indices comprises a first risk index value based on a total volume of identified hotspots within one or more of the identified 3D boundaries.
2 more elements
A network-based decision support system comprising:
- a processor;
- and
- a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:
- (i) receive and store a plurality of medical images in a database, each medical image associated with a corresponding patient;
- (ii) access one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprises a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient, said PET scan obtained following administration of an imaging agent comprising a radionuclide labelled prostate specific membrane antigen (PSMA) binding agent to the first patient, wherein the radionuclide labelled PSMA binding agent comprises [18F]DCFPyL;
- (iii) automatically segment the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more regions of imaged tissue;
- and
- (iv) compute, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue, a risk map comprising graphical denotations marking regions of risk of cancer or risk of recurrence of cancer.
5 more elements
A method comprising:
- (i) receiving and storing, by a processor of a computing device, a plurality of medical images in a database, each medical image associated with a corresponding patient;
- (ii) accessing, by the processor, one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprises a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient, said PET scan obtained following administration of an imaging agent comprising a radionuclide labelled prostate specific membrane antigen (PSMA) binding agent to the first patient, wherein the radionuclide labelled PSMA binding agent comprises [18F]DCFPyL;
- (iii) automatically segmenting, by the processor, the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more regions of imaged tissue;
- and
- (iv) computing, by the processor, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue, a risk map comprising graphical denotations marking regions of risk of cancer or risk of recurrence of cancer.
2 more elements
A network-based decision support system comprising:
- a processor;
- and
- a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:
- (a) receive and store a plurality of medical images in a database, each medical image associated with a corresponding patient;
- (b) access one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprises a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient;
- (c) automatically segment the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more regions of imaged tissue;
- and
- (d) transfer the identified 3D boundaries to the PET scan, identify one or more hotspots within the PET scan, and compute, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue:
- (i) a risk map comprising graphical denotations marking regions of risk of cancer or risk of recurrence of cancer and (ii) one or more risk index values, each risk index value indicative of a cancer state or progression in the first patient;
- wherein, the one or more risk index values comprises a first risk index value based on a total volume of identified hotspots within one or more of the identified 3D boundaries.
7 more elements
A method comprising:
- (a) receiving and storing, by a processor of a computing device, a plurality of medical images in a database, each medical image associated with a corresponding patient;
- (b) accessing, by the processor, one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprises a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient;
- (c) automatically segmenting, by the processor, the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more regions of imaged tissue;
- and
- (d) transferring, by the processor, the identified 3D boundaries to the PET scan, identifying, by the processor, one or more hotspots within the PET scan, and computing, by the processor, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue:
- (i) a risk map comprising graphical denotations marking regions of risk of cancer or risk of recurrence of cancer and (ii) one or more risk index values, each risk index value indicative of a cancer state or progression in the first patient, wherein the one or more risk index values comprises a first risk index value based on a total volume of identified hotspots within one or more of the identified 3D boundaries.
3 more elements
A network-based decision support system comprising:
- a processor;
- and
- a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:
- (i) receive and store a plurality of medical images in a database, each medical image associated with a corresponding patient;
- (ii) access one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprises a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient;
- (iii) automatically segment the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more region(s) of imaged tissue;
- and
- (iv) transfer the identified 3D boundaries to the PET scan, identify one or more hotspots within the PET scan, and compute, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue, one or more risk index values, each risk index value indicative of a cancer state or progression in the first patient, wherein the one or more risk index values comprises a first risk index value based on a total number of identified hotspots within one or more of the identified 3D boundaries.
5 more elements
A method comprising:
- (i) receiving and storing, by a processor of a computing device, a plurality of medical images in a database, each medical image associated with a corresponding patient;
- (ii) accessing, by the processor, one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprise a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient;
- (iii) automatically segmenting, by the processor, the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more regions of imaged tissue;
- and
- (iv) transferring, by the processor, the identified 3D boundaries to the PET scan, identifying, by the processor, one or more hotspots within the PET scan, and computing, by the processor, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue, a value of each of one or more risk indices, each risk index value indicative of a cancer state or progression in the first patient, wherein the one or more risk indices comprises a first risk index value based on a total number of identified hotspots within one or more of the identified 3D boundaries.
2 more elements
Dependencies are inferred from the claim text.