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：US20210118549A1

公开(公告)日：2021-04-22

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 ,  Jinyoung Kim

IPC分类号： G16H30/20 ,  A61B6/00 ,  G06F16/50 ,  G06K9/00 ,  G06K9/62 ,  G06N5/04 ,  G06N20/00 ,  G06T7/00 ,  G06T7/11 ,  G06T7/33 ,  G16H50/20 ,  A61B5/055

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：US20240185430A1

公开(公告)日：2024-06-06

申请号：US18282695

申请日：2022-03-18

申请人： Owl Navigation, Inc.

发明人： Dan Askarov ,  Alan Lund ,  Guillermo Sapiro ,  Noam Harel

IPC分类号： G06T7/174 ,  G06T7/00 ,  G06T7/11 ,  G06T7/149

CPC分类号： G06T7/174 ,  G06T7/0014 ,  G06T7/11 ,  G06T7/149 ,  G06T2207/10096 ,  G06T2207/10144 ,  G06T2207/20016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30008 ,  G06T2207/30168

摘要： Disclosed embodiments include methods and computer systems for brain image prediction or segmentation. A clinical image file of data representative of a patients' brain image, including structures of interest (SOI) such as the subthalamic nucleus (STN), is applied to and processed by a segmentation process. The segmentation process uses one or more machine learning approaches such as trained deep learning models to identify the SOI in the clinical image. Output by the segmentation process is a segmented image file of data representing the brain image in which the structures of interest (SOI) are segmented. By the segmentation process, the SOI in clinical image, including the locations, orientations and/or boundaries of the SOI, are accurately predicted or identified, and can thereby be presented in an enhanced visualization form (e.g., highlighted) in the segmented image.

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.