1. A method for providing a personalized network based dialogue, comprising:
storing a script associated with a campaign, the script specifying a plurality of events, and a set of user information for a user selected for the campaign in a computer coupled to a network, the computer comprising a processor and a memory storing a set of instructions for:
associating a variable with a first event specified by the script;
executing a first instruction according to the script, wherein the first instruction is executable to send a first communication to the user from a server via the network;
assigning a value to the variable;
determining a second instruction according to the script based on the stored set of user information and a value of the variable associated with the first event; and
executing the second instruction to send a second communication to the user from the server.
2. The method of claim 1, wherein the value is assigned to the variable based on one or more of an interaction with the user, a communication with the user, a time between communications with the user or a number of communications with the user.
3. The method of claim 2, wherein the second instruction is further determined based on a priority of a message.
4. The method of claim 2, wherein the second instruction is further determined based on a permission associated with the user.
5. The method of claim 2, wherein assigning the value to the variable causes the script to follow one of a plurality of paths.
6. The method of claim 5, wherein at least one path comprises a loop.
7. The method of claim 1, further comprising specifying a second event in the script and associating a second variable with the second event, wherein the second instruction is based on a specified combination of a value of the second variable and the value of the variable associated with the first event.
8. An electronic personalization dialog system comprising:
a data storage location;
a dialog computer interfaced with the data storage location and a communications channel, the dialog computer comprising a processor and a non-transitory tangible computer readable medium storing instructions executable to:
store a script associated with a campaign, the script specifying a plurality of events, and a set of user information for a user selected for the campaign in a computer coupled to a network, the computer comprising a processor and a memory storing a set of instructions for:
associate a variable with a first event specified by the script;
execute a first instruction according to the script, wherein the first instruction is executable to send a first communication to the user from a server via the network;
assign a value to variable;
determine a second instruction according to the script based on the stored set of user information and the value of the variable associated with the first event; and
execute the second instruction to send a second communication to the user.
9. The electronic personalization dialog system of claim 8, wherein the value is assigned to the variable associated with the first event based on one or more of an interaction with the user, a communication with the user, a time between communications with the user or a number of communications with the user.
10. The electronic personalization dialog system of claim 9, wherein the second instruction is further determined based on a priority of a message.
11. The electronic personalization dialog system of claim 9, wherein assigning the value to the variable causes the script to follow one of a plurality of paths.
12. The electronic personalization dialog system of claim 11, wherein at least one path comprises a loop.
13. The electronic personalization dialog system of claim 9, wherein the second instruction is further determined based on a permission associated with the user.
14. A computer program product comprising a non-transitory storage medium storing computer program instructions executable to perform a method comprising:
storing a script associated with a campaign, the script specifying a plurality of events, and a set of user information for a user selected for the campaign in a computer coupled to a network, the computer comprising a processor and a memory storing a set of instructions for:
associating a variable with a first event specified by the script;
executing a first instruction according to the script, wherein the first instruction is executable to send a first communication to the user from a server via the network;
assigning a value to the variable;
determining a second instruction according to the script based on the stored set of user information and the value of the variable associated with the first event; and
executing the second instruction to send a second communication to the user.
15. The computer program product of claim 14, wherein the value is assigned based on one or more of an interaction with the user, a communication with the user, a time between communications with the user or a number of communications with the user.
16. The computer program product of claim 15, wherein the second instruction is further determined based on a priority of a message.
17. The computer program product of claim 15, wherein assigning the value to the variable causes the script to follow one of a plurality of paths.
18. The computer program product of claim 17, wherein at least one path comprises a loop.
19. The computer program product of claim 15, wherein the second instruction is further determined based on a permission associated with the user.
20. The computer program product of claim 15, further comprising associating a second variable with a second event, wherein the second instruction is based on a specified combination of a value of the second variable and the value of the variable associated with the first event.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for providing automatic diagnosis and decision support in whole-body imaging, comprising:
using whole-body imaging to obtain a first set of image data of a patient;
fitting a statistical whole-body atlas using the first set of image data, wherein the statistical whole-body atlas includes at least one of statistics on voxel intensities, statistics on global and local shape deformations, or statistics on joint articulations; and
using the statistical whole-body atlas to characterize pathological findings in terms of a diagnosis.
2. The method of claim 1, wherein the first set of image data is obtained using one or more imaging modalities.
3. The method of claim 1, wherein the first set of image data is obtained using whole-body positron emission tomography (PET) and one or more imaging modalities other than whole-body PET.
4. The method of claim 1, wherein the statistical whole-body atlas comprises at least one of distributional appearance models or spatial relational models of organs or structures in one or more whole-body imaging modalities.
5. The method of claim 1, wherein the first set of image data includes PET data, and wherein fitting the statistical whole-body atlas comprises automatically outlining selected regions of interest with pathological tracer uptakes while discounting physiological uptakes in the PET data.
6. The method of claim 1, wherein the first set of image data further includes image data acquired by one or more imaging modalities other than PET, and wherein using the statistical whole-body atlas to characterize pathological findings in terms of a diagnosis comprises detecting anatomical or functional abnormalities of the whole body or body parts using the first set of image data.
7. The method of claim 1, wherein using the statistical whole-body atlas to characterize pathological findings in terms of a diagnosis comprises automatically outlining selected regions of interest in the first set of image data and automatically extracting candidate features of interest from the selected regions of interest.
8. The method of claim 7, wherein using the statistical whole-body atlas to characterize pathological findings in terms of a diagnosis further comprises automatically contouring and characterizing each of the candidate features of interest.
9. The method of claim 8, wherein automatic contouring and characterizing is based on at least one of a standard uptake value or brain uptake normalized data.
10. The method of claim 1, wherein the first set of image data comprises at least one of 2-D image data, 3-D image data or higher-dimensional image data.
11. The method of claim 1 further comprising:
using whole-body imaging to obtain a second set of image data of the patient; and
updating pathological findings based on the statistical whole-body atlas using the second set of image data.
12. The method of claim 11, wherein updating pathological findings based on the statistical whole-body atlas comprises encoding anatomical or functional variations of the whole body or body parts using the second set of image data.
13. The method of claim 11 further comprising:
using whole-body imaging to obtain a third set of image data of the patient; and
updating pathological findings based on the statistical whole-body atlas using the third set of image data.
14. The method of claim 13, wherein updating pathological findings based on the statistical whole-body atlas comprises encoding anatomical or functional variations of the whole body or body parts using the third set of image data.
15. A method for providing automatic diagnosis and decision support in whole body imaging, comprising:
detecting and segmenting one or more selected regions of interest in whole-body images;
detecting one or more abnormalities by automatically interpreting whole-body images of the selected regions of interest for pathological findings; and
characterizing the pathological findings in terms of a diagnosis.
16. The method of claim 15, wherein segmenting the selected regions of interest is accomplished using at least one of bounding boxes, centroid locations, bounding surfaces, or a bounding mask.
17. The method of claim 15, further comprising detecting hotspots in the selected regions of interest.
18. The method of claim 17, wherein the hotspots are segmented using organ-specific thresholds.
19. The method of claim 15, wherein the whole-body images are obtained using at least one of whole-body PET, CT, MRI, SPECT, PETCT, SPECTCT, or PETMRI.
20. The method of claim 15, wherein, when a longitudinal data is available, performing clinical analysis of the longitudinal data and outputting changes in a clinically meaningful way.
21. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for providing automatic diagnosis and decision support, the method steps comprising:
using whole-body imaging to obtain a first set of image data of a patient;
fitting a statistical whole-body atlas using the first set of image data, wherein the statistical whole-body atlas includes at least one of statistics on voxel intensities, statistics on global and local shape deformations, or statistics on joint articulations; and
using the statistical whole-body atlas to characterize pathological findings in terms of a diagnosis.
22. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for providing automatic diagnosis and decision support, the method steps comprising:
detecting and segmenting one or more selected regions of interest in whole-body images;
detecting one or more abnormalities by automatically interpreting whole-body images of the selected regions of interest for pathological findings; and
characterizing the pathological findings in terms of a diagnosis.