公开(公告)号：US20150086101A1

公开(公告)日：2015-03-26

申请号：US14496639

申请日：2014-09-25

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Roshni Bhagalia ,  Qi Song ,  Albert Amos Montillo

IPC: A61B6/00 ,  G06K9/46 ,  G06T7/00 ,  A61B6/03

CPC classification number: A61B6/5241 ,  A61B6/032 ,  A61B6/463 ,  A61B6/469 ,  A61B6/488 ,  A61B6/503 ,  A61B6/542 ,  G06K2009/4666 ,  G06K2209/051

Abstract: In some embodiments, a method for localizing organs in anatomical imaging may include: performing an anterior-posterior view scan and a lateral view scan to create an anterior-posterior view scan image and a lateral view scan image; creating a joint anatomical model based on the anterior-posterior scan image and the lateral view scan image; and refining the joint anatomical model.

Abstract translation: 在一些实施例中，用于在解剖学成像中定位器官的方法可以包括：执行前 - 后视图扫描和侧视图扫描以创建前 - 后视图扫描图像和侧视图扫描图像; 基于前后扫描图像和侧视图扫描图像创建关节解剖模型; 并完善关节解剖模型。

公开(公告)号：US09349186B2

公开(公告)日：2016-05-24

申请号：US14177414

申请日：2014-02-11

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Xiaofeng Liu ,  John Frederick Schenck ,  Ek Tsoon Tan ,  Albert Amos Montillo

IPC: G06K9/00 ,  G06T7/00

CPC classification number: G06T7/0081 ,  G06T7/11 ,  G06T7/143 ,  G06T7/149 ,  G06T7/174 ,  G06T2200/04 ,  G06T2207/10072 ,  G06T2207/20076 ,  G06T2207/20128 ,  G06T2207/30016

Abstract: The system and method of the invention combines target image intensity into a maximum likelihood estimate (MLE) framework as in STAPLE to take advantage of both intensity-based segmentation and statistical label fusion based on atlas consensus and performance level, abbreviated iSTAPLE. The MLE framework is then solved using a modified expectation-maximization algorithm to simultaneously estimate the intensity profiles of structures of interest as well as the true segmentation and atlas performance level. The iSTAPLE greatly extends the use of atlases such that the target image need not have the same image contrast and intensity range as the atlas images.

Abstract translation: 本发明的系统和方法将目标图像强度与STAPLE中的最大似然估计（MLE）框架相结合，以利用基于图集共识和性能水平（简称为iSTAPLE）的基于强度的分割和统计标签融合。 然后使用修改的期望最大化算法来解决MLE框架，以同时估计感兴趣的结构的强度分布以及真实的分段和图谱性能水平。 iSTAPLE大大扩展了地图集的使用，使得目标图像不需要具有与图集图像相同的图像对比度和强度范围。

公开(公告)号：US20140226889A1

公开(公告)日：2014-08-14

申请号：US14177414

申请日：2014-02-11

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Xiaofeng Liu ,  John Frederick Schenck ,  Ek Tsoon Tan ,  Albert Amos Montillo

IPC: G06T7/00

CPC classification number: G06T7/0081 ,  G06T7/11 ,  G06T7/143 ,  G06T7/149 ,  G06T7/174 ,  G06T2200/04 ,  G06T2207/10072 ,  G06T2207/20076 ,  G06T2207/20128 ,  G06T2207/30016

Abstract: The system and method of the invention combines target image intensity into a maximum likelihood estimate (MLE) framework as in STAPLE to take advantage of both intensity-based segmentation and statistical label fusion based on atlas consensus and performance level, abbreviated iSTAPLE. The MLE framework is then solved using a modified expectation-maximization algorithm to simultaneously estimate the intensity profiles of structures of interest as well as the true segmentation and atlas performance level. The iSTAPLE greatly extends the use of atlases such that the target image need not have the same image contrast and intensity range as the atlas images.

Abstract translation: 本发明的系统和方法将目标图像强度与STAPLE中的最大似然估计（MLE）框架相结合，以利用基于图集共识和性能水平（简称为iSTAPLE）的基于强度的分割和统计标签融合。 然后使用修改的期望最大化算法来解决MLE框架，以同时估计感兴趣的结构的强度分布以及真实的分段和图谱性能水平。 iSTAPLE大大扩展了地图集的使用，使得目标图像不需要具有与图集图像相同的图像对比度和强度范围。