A volumetric segmentation method is disclosed for brain region analysis, in particular but not limited to, regions of the basal ganglia such as the subthalamic nucleus (STN). This serves for visualization and localization within the sub-cortical region of the basal ganglia, as an example of prediction of a region of interest for deep brain stimulation procedures. A statistical shape model is applied for variation modes of the STN, or the corresponding regions of interest, and its predictors on high-quality training sets obtained from high-field, e.g., 7T, MR imaging. The partial least squares regression (PLSR) method is applied to induce the spatial relationship between the region to be predicted, e.g., STN, and its predictors. The prediction accuracy for validating the invention is evaluated by measuring the shape similarity and the errors in position, size, and orientation between manually segmented STN and its predicted one.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for operating an electronic device to generate a database of brain images, comprising: receiving and storing a plurality of brain image training sets; processing, using a computer, each of the brain image training sets to segment a region of interest within the training set and one or more adjacent predictors in the training set; registering, using a computer, each training set and the segmented region of interest and one or more predictors for the training set; and storing the registered training set, region of interest and one or more predictors of each brain image training set. 2. The method of claim 1 wherein the brain image training sets include magnetic resonance images taken with a field strength of about 7 Tesla or higher. 3. The method of claim 1 wherein registering the segmented region of interest and one or more predictors for each training set includes normalizing the segmented region of interest and one or more predictors. 4. The method of claim 1 wherein registering the segmented region of interest and one or more predictors for each training set includes transforming the region of interest and one or more predictors into a common coordinate space. 5. The method of claim 1 wherein segmenting the region of interest and one or more predictors includes generating data representative of one or more of the shape, location, size or orientation for the region of interest and one or more predictors. 6. The method of claim 1 wherein processing each brain image training set to segment the region of interest and one or more predictors includes manually operating a user interface. 7. The method of claim 1 wherein segmenting the region of interest and one or more predictors includes segmenting subcortical structures. 8. The method of claim 7 wherein segmenting subcortical structures includes segmenting a subthalamic nucleus as the region of interest, and segmenting one of more of the Substantia Nigra, Red Nucleus, internal Globus Pallidus and Thalamus as the one or more predictors. 9. The method of claim 1 wherein registering each training set includes registering the training set to a coordinate system. 10. The method of claim 1 wherein registering each training set includes registering at least portions of the training set to at least portions of other stored training sets. 11. The method of claim 1 and further including storing a plurality of metadata elements associated with each of the training sets, wherein the metadata elements describe one or more of sex, age, medical history including diagnosis of a particular illness, brain size, brain dimensions and imaging modality associated with the one or more additional brain images. 12. The method of claim 1 wherein receiving and storing a plurality of brain image training sets includes receiving and storing brain image training sets produced using a plurality of imaging modalities. 13. The method of claim 12 wherein receiving and storing a plurality of brain image training sets includes receiving and storing magnetic resonance images taken with a field strength of about 7 Tesla or higher, magnetic resonance images taken with a field strength of about 3 Tesla or lower, and optionally computed tomography images. 14. A method for operating an electronic device to generate a database of brain images, comprising: receiving and storing in memory: first imaging modality brain image sets from each of a plurality of individuals; second imaging modality brain image sets from at least some of the plurality of individuals for which the first modality image sets are stored, wherein the second modality is different than the first modality; and optionally additional modality brain image sets from at least some of the plurality of individuals for which the first modality image sets are stored, wherein the additional modality is different than the first and second modalities; processing using a computer: each first modality image set to segment a region of interest within the image set and one or more adjacent predictors in the image set; and each second modality image set and any additional modality image set to segment one or more predictors corresponding to the one or more predictors in the first modality image sets; registering using a computer: each first modality image set and the segmented region of interest and one or more predictors for the image set; and each second modality image set and any additional modality image set, and the segmented one or more predictors for each second modality image set and any additional modality image set; and storing in memory: each registered first modality image set, region of interest and one or more predictors, and metadata elements associated with each first modality image set; and each registered second modality image set and any additional modality image set, the one or more predictors for each second modality image set and any additional modality image set, and metadata elements associated with each second modality image set and any additional modality image set. 15. The method of claim 14 wherein: receiving the first imaging modality image sets includes receiving magnetic resonance images taken with a field strength of about 7 Tesla or higher; receiving the second imaging modality image sets includes receiving magnetic resonance images taken with a field strength of about 3 Tesla or lower; and receiving optionally additional imaging modality image sets includes optionally receiving computed tomography images. 16. The method of claim 15 wherein: registering each second modality image set includes registering the second modality image set of each individual to the first modality image set of the same individual; and registering any optional additional modality image set includes registering the additional modality image set of each individual to the first modality image set of the same individual. 17. The method of claim 16 wherein registering each first, second and any additional modality image set includes registering each first, second and any additional modality image set to a coordinate system. 18. The method of claim 15 and further including: processing the segmented regions of interest and predictors of magnetic resonance images taken with a field strength of about 7 Tesla or higher to generate a statistical model representative of relationships between regions of interest and predictors in the images; and storing the statistical model in memory. 19. The method of claim 18 wherein the statistical model is a shape model of the region of interest. 20. The method of claim 14 and further including: processing the segmented regions of interest and predictors of the first modality images to generate a statistical model representative of relationships between regions of interest and predictors in the images; and storing the statistical model in memory. 21. The method of claim 20 wherein the statistical model is a shape model of the region of interest. 22. The method of claim 14 wherein the metadata elements describe one or more of sex, age, medical history including diagnosis of a particular illness, brain size and brain dimensions. 23. A method for operating an electronic device to generate a patient-specific brain image atlas, comprising: receiving a patient's brain image produced using an imaging modality, wherein the patient's brain image is associated with a patient having characteristics; accessing a database of additional brain images including images different than the patient's brain image, wherein the database includes: registered first imaging modality brain image sets and associated metadata from each of a plurality of individuals; registered second imaging modality brain image sets and associated metadata from at least some of the plurality of individuals, wherein the second modality is different than the first modality; and optionally registered additional modality brain image sets and associated metadata from at least some of the plurality of individuals, wherein the additional modality is different than the first and second modalities; selecting one or more of the additional brain images from the database based on a correlation of the metadata elements of the one or more additional images to the patient's characteristics and optionally the imaging modality of the patient's brain image; processing, using the electronic device, the patient's brain image to segment one or more predictors corresponding to registered predictors of the selected one or more additional brain images; processing, using the electronic device, the patient's brain image and the selected one or more additional brain images as a function of the segmented one or more predictors in the patient's brain image and registered predictors of the selected one or more additional brain images, to predict one or more of the shape, location, size or orientation of a region of interest in the patient's brain image corresponding to registered regions of interest in any of the selected one or more additional brain images; and processing, using the electronic device, the patient's brain image as a function of the predicted one or more of the shape, location, size or orientation of the region of interest to incorporate the predicted one or more of the shape, location, size or orientation of the region of interest into the patient's brain image to generate a patient-specific atlas. 24. The method of claim 23 wherein accessing a database of additional brain images includes: accessing a database wherein: the registered first imaging modality brain image sets include magnetic resonance images taken with a field strength of about 7 Tesla or higher; the registered second imaging modality brain image sets include magnetic resonance images taken with a field strength of about 3 Tesla or lower; and the optional registered additional modality brain image sets include computed tomography images. 25. The method of claim 24 wherein: receiving a patient's brain image includes receiving a magnetic resonance image taken with a field strength of about 3 Tesla or lower; and selecting one or more of the additional brain images from the database includes selecting one of the magnetic resonance images taken with a field strength of about 7 Tesla or higher. 26. The method of claim 25 wherein selecting one or more additional brain images further includes selecting a computed tomography image. 27. The method of claim 25 wherein the region of interest is a subthalamic nucleus and the one or more predictors include one or more of the Substantia Nigra, Red Nucleus, internal Globus Pallidus and Thalamus. 28. The method of claim 24 wherein receiving a patient's brain image further includes receiving a computed tomography image. 29. The method of claim 23 wherein processing the patient's brain image to predict one or more of the shape, location, size or orientation of the region of interest further includes processing the patient's brain image as a function of spatial relationships between registered regions of interest and predictors of the selected one or more additional brain images. 30. The method of claim 29 wherein processing the patient's brain image to predict one or more of the shape, location, size or orientation of the region of interest further includes predicting one or more of the shape, location, size or orientation of the region of interest as a function of spatial relationships between registered regions of interest and predictors of the selected one or more additional brain images and the segmented region of interest in the patient's brain image. 31. The method of claim 23 wherein: the method further includes accessing a database including a stored statistical model representative of relationships between regions of interest and predictors corresponding to the regions of interest and predictors of images in the database; and processing the patient's brain image to predict one or more of the shape, location, size or orientation of the region of interest further includes processing as a function of the statistical model. 32. The method of claim 23 wherein accessing a stored statistical model includes accessing a shape model of the region of interest. 33. The method of claim 32 wherein processing the patient's brain image to generate the patient-specific atlas includes processing the patient's brain image as a function of the shape model to incorporate the predicted one or more of the shape, location, size or orientation of the region of interest into the patient's brain image to generate a patient-specific atlas. 34. The method of claim 33 wherein the region of interest is a subthalamic nucleus and the one or more predictors include one or more of the Substantia Nigra, Red Nucleus, internal Globus Pallidus and Thalamus. 35. The method of claim 23 wherein processing the patient's brain image to produce a patient-specific atlas includes removing portions of at least some of the brain image and the selected at least one first modality image set and any one or more second and optionally additional modality image sets. 36. The method of claim 23 wherein the metadata elements describe one or more of sex, age, medical history including diagnosis of a particular illness, brain size and brain dimensions.