公开(公告)号：US11771389B2

公开(公告)日：2023-10-03

申请号：US17137022

申请日：2020-12-29

申请人： Owl Navigation Inc.

发明人： Guillermo Sapiro ,  Noam Harel ,  Yuval Duchin ,  Jin Young Kim

IPC分类号： A61B6/00 ,  G06N20/00 ,  G06F16/50 ,  A61B5/055 ,  G06T7/33 ,  G06T7/11 ,  G16H30/20 ,  G06F18/25 ,  G06F18/00 ,  G16H50/20 ,  G06N5/04 ,  G06T7/00 ,  A61B6/03 ,  A61B6/12 ,  A61B5/00 ,  A61B34/10

CPC分类号： A61B6/501 ,  A61B5/055 ,  A61B6/5217 ,  A61B6/5294 ,  G06F16/50 ,  G06F18/00 ,  G06F18/251 ,  G06N5/04 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/337 ,  G16H30/20 ,  G16H50/20 ,  A61B5/0035 ,  A61B6/032 ,  A61B6/037 ,  A61B6/12 ,  A61B6/469 ,  A61B6/5247 ,  A61B6/563 ,  A61B2034/107 ,  A61B2576/026 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20128 ,  G06T2207/30016

摘要： 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.

公开(公告)号：US10885149B2

公开(公告)日：2021-01-05

申请号：US15457355

申请日：2017-03-13

申请人： Owl Navigation, Inc.

发明人： Guillermo Sapiro ,  Noam Harel ,  Yuval Duchin ,  Jin Young Kim

IPC分类号： G06N20/00 ,  G06F19/00 ,  G06F16/50 ,  G06K9/62 ,  A61B6/00 ,  A61B5/055 ,  G06T7/33 ,  G06T7/11 ,  G06K9/00 ,  G16H50/20 ,  G06N5/04 ,  G06T7/00 ,  A61B6/03 ,  A61B6/12 ,  A61B5/00

摘要： 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.

公开(公告)号：US20230293130A1

公开(公告)日：2023-09-21

申请号：US18140437

申请日：2023-04-27

申请人： Owl Navigation, Inc.

发明人： Guillermo Sapiro ,  Noam Harel ,  Yuval Duchin ,  Jin Young Kim

IPC分类号： A61B6/00 ,  G06N20/00 ,  G06F16/50 ,  A61B5/055 ,  G06T7/33 ,  G06T7/11 ,  G16H30/20 ,  G06F18/25 ,  G06F18/00 ,  G16H50/20 ,  G06N5/04 ,  G06T7/00

CPC分类号： A61B6/501 ,  A61B5/055 ,  A61B6/5217 ,  A61B6/5294 ,  G06F16/50 ,  G06F18/00 ,  G06F18/251 ,  G06N5/04 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/337 ,  G16H30/20 ,  G16H50/20 ,  A61B6/032 ,  G06T2207/10088 ,  G06T2207/20128 ,  G06T2207/30016

摘要： 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., 7 T, 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.

公开(公告)号：US12048574B2

公开(公告)日：2024-07-30

申请号：US18140437

申请日：2023-04-27

申请人： Owl Navigation, Inc.

发明人： Guillermo Sapiro ,  Noam Harel ,  Yuval Duchin ,  Jin Young Kim

IPC分类号： A61B6/50 ,  A61B5/00 ,  A61B5/055 ,  A61B6/00 ,  A61B6/03 ,  A61B6/12 ,  A61B6/46 ,  A61B34/10 ,  G06F16/50 ,  G06F18/25 ,  G06N5/04 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/33 ,  G16H30/20 ,  G16H50/20

CPC分类号： A61B6/501 ,  A61B5/055 ,  A61B6/5217 ,  A61B6/5294 ,  G06F16/50 ,  G06F18/251 ,  G06N5/04 ,  G06N20/00 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/337 ,  G16H30/20 ,  G16H50/20 ,  A61B5/0035 ,  A61B6/032 ,  A61B6/037 ,  A61B6/12 ,  A61B6/469 ,  A61B6/5247 ,  A61B6/563 ,  A61B2034/107 ,  A61B2576/026 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20128 ,  G06T2207/30016

摘要： 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.