System and method for facilitating dbs electrode trajectory planning

Abstract

A system and method for facilitating DBS electrode trajectory planning using a machine learning (ML)-based feature identification scheme configured to identify and distinguish between various regions of interest (ROIs) and regions of avoidance (ROAs) in a patient's brain scan image. In one arrangement, standard orientation image slices as well as re-sliced images in non-standard orientations are provided in a labeled input dataset for training a CNN/ANN for distinguishing between ROIs and ROAs. Upon identification of the ROIs and ROAs in the patient's brain scan image, an optimal trajectory for implanting a DBS lead may be determined relative to a particular ROI while avoiding any ROAs.

1 . A computer-implemented method for identifying targets of interest (TOIs) for deep brain stimulation (DBS), the method comprising:
 obtaining a set of medical imaging data pertaining to human cranial anatomy, the set of medical imaging data sampled from a plurality of humans in one or more imaging modalities, wherein the medical imaging data comprises image slices taken along at least one of coronal, sagittal and axial planes relative to the human cranial anatomy;   re-slicing at least a portion of the medical imaging data through one or more planes that are at an angular orientation with respect to at least one of the coronal, sagittal and axial planes, thereby obtaining re-sliced medical imaging data;   training a first artificial neural network (ANN) engine using a portion of the medical imaging data that has not been re-sliced and a portion of the re-sliced medical imaging data, wherein the medical imaging data is appropriately labeled, to obtain a validated and tested ANN engine configured to identify one or more TOIs in a human brain image; and   executing the first ANN engine, in response to an input image of a patient's brain obtained using a particular imaging modality, to identify at least one particular TOI in the patient's brain for DBS.   
     
     
         2 . The method as recited in  claim 1 , further comprising:
 blending two or more co-registered image slices selected from at least one of the medical imaging data that has not been re-sliced or the portion of the re-sliced medical imaging data to obtain hybrid image slices;   training the first ANN engine using a portion of the hybrid image slices in addition to the medical imaging data that has not been re-sliced and the portion of the re-sliced medical imaging data.   
     
     
         3 . The method as recited in  claim 1 , further comprising:
 blending two or more co-registered image slices selected from at least one of the medical imaging data that has not been re-sliced or the portion of the re-sliced medical imaging data to obtain hybrid image slices;   training a second ANN engine using a portion of the hybrid image slices to obtain a validated and tested ANN engine configured to identify one or more TOIs in the human brain image;   executing the first and second ANN engines separately with respect to the input image of the patient's brain and combining the TOI identifications obtained respectively therefrom for improving quality of identification of the at least one particular TOI.   
     
     
         4 . The method as recited in  claim 1 , further comprising performing, prior to the training, morphological image processing of image slices of the medical imaging data that has not been re-sliced or the portion of the re-sliced medical imaging data, wherein the morphological image processing includes at least one of edge detection, contrast boosting and shape detection. 
     
     
         5 . The method as recited in  claim 1 , further comprising performing a dropout technique with respect to the first ANN engine wherein a select number of computational nodes are dropped from a particular neural network layer in each training epoch. 
     
     
         6 . The method as recited in  claim 1 , further comprising:
 building an electrode scene with respect to the at least one particular TOI of the patient's brain image for placing a DBS lead thereat; and   determining an optimal trajectory for implanting the DBS lead in the patient's brain relative to a particular electrode of the DBS lead.   
     
     
         7 . The method as recited in  claim 6 , further comprising:
 co-registering a computed tomography (CT) image of the patient's brain with the input image of the patient having the at least one particular TOI identified for stimulation, wherein the input image of the patient's brain comprises one of a pre-operative or intra-operative magnetic resonance imaging (MRI) scan; and   obtaining an entry point coordinate set and a target point coordinate set with respect to the patient's brain for performing an implant procedure to implant the DBS lead using the optimal trajectory, wherein the entry point coordinate set is operative to identify a burr hole location on the patient's cranium and the target point coordinate set is operative to identify a location relative to the at least one particular TOI in the patient's brain.   
     
     
         8 . The method as recited in  claim 7 , further comprising:
 providing the entry point coordinate set, the target point coordinate set and data relating to the optimal trajectory to a stereotactic surgery system including a guiding apparatus containing the DBS lead; and   automatically guiding the DBS lead to the at least one particular TOI based on the entry point coordinate set, the target point coordinate set and the data relating to the optimal trajectory data to place the particular electrode proximate to the at least one particular TOI.   
     
     
         9 . A computer-implemented system configured to facilitate identifying targets of interest (TOI) for deep brain stimulation (DBS), the system comprising:
 one or more processors; and   a persistent memory having program instructions stored thereon, the program instructions, when executed by the one or more processors, configured to perform:
 obtaining a set of medical imaging data pertaining to human cranial anatomy, the set of medical imaging data sampled from a plurality of humans in one or more imaging modalities, wherein the medical imaging data comprises image slices taken along at least one of coronal, sagittal and axial planes relative to the human cranial anatomy; 
 re-slicing at least a portion of the medical imaging data through one or more planes that are at an angular orientation with respect to at least one of the coronal, sagittal and axial planes, thereby obtaining re-sliced medical imaging data; 
 training a first artificial neural network (ANN) engine using a portion of the medical imaging data that has not been re-sliced and a portion of the re-sliced medical imaging data, wherein the medical imaging data is appropriately labeled, to generate a validated and tested ANN engine configured to identify one or more TOIs in a human brain image; and 
 in response to an input image of a patient's brain obtained using a particular imaging modality, executing the first ANN engine to identify at least one particular TOI in the patient's brain for DBS. 
   
     
     
         10 . The system as recited in  claim 9 , wherein the program instructions further comprise instructions configured to perform:
 blending two or more co-registered image slices selected from at least one of the medical imaging data that has not been re-sliced or the portion of the re-sliced medical imaging data to generate hybrid image slices;   training the first ANN engine using a portion of the hybrid image slices in addition to the medical imaging data that has not been re-sliced and the portion of the re-sliced medical imaging data.   
     
     
         11 . The system as recited in  claim 9 , wherein the program instructions further comprise instructions configured to perform:
 blending two or more co-registered image slices selected from at least one of the medical imaging data that has not been re-sliced or the portion of the re-sliced medical imaging data to obtain hybrid image slices;   training a second ANN engine using a portion of the hybrid image slices to generate a validated and tested ANN engine configured to identify one or more TOIs in the human brain image;   executing the first and second ANN engines separately with respect to the input image of the patient's brain and combining the TOI identifications obtained respectively therefrom for improving quality of identification of the at least one particular TOI.   
     
     
         12 . The system as recited in  claim 9 , wherein the program instructions further comprise instructions configured to perform, prior to the training, morphological image processing of image slices of the medical imaging data that has not been re-sliced or the portion of the re-sliced medical imaging data, wherein the morphological image processing includes at least one of edge detection, contrast boosting and shape detection. 
     
     
         13 . The system as recited in  claim 9 , wherein the program instructions further comprise instructions configured to perform a dropout technique with respect to the first ANN engine wherein a select number of computational nodes are dropped from a particular neural network layer in each training epoch. 
     
     
         14 . The system as recited in  claim 9 , wherein the program instructions further comprise instructions configured to perform:
 building an electrode scene with respect to the at least one particular TOI of the patient's brain image for placing a DBS lead thereat; and   determining an optimal trajectory for implanting the DBS lead in the patient's brain relative to a particular electrode of the DBS lead.   
     
     
         15 . The system as recited in  claim 14 , wherein the program instructions further comprise instructions configured to perform:
 co-registering a computed tomography (CT) image of the patient's brain with the input image of the patient having the at least one particular TOI identified for stimulation, wherein the input image of the patient's brain comprises one of a pre-operative or intra-operative magnetic resonance imaging (MRI) scan; and   determining an entry point coordinate set and a target point coordinate set with respect to the patient's brain for performing an implant procedure to implant the DBS lead using the optimal trajectory, wherein the entry point coordinate set is operative to identify a burr hole location on the patient's cranium and the target point coordinate set is operative to identify a location relative to the at least one particular TOI in the patient's brain.   
     
     
         16 . The system as recited in  claim 15 , further comprising a stereotactic surgery system including a guiding apparatus containing the DBS lead, and wherein the program instructions further comprise instructions configured to perform:
 providing the entry point coordinate set, the target point coordinate set and data relating to the optimal trajectory to the stereotactic surgery system; and   automatically guiding the DBS lead to the at least one particular TOI based on the entry point coordinate set, the target point coordinate set and the data relating to the optimal trajectory data to place the particular electrode proximate to the at least one particular TOI.