公开(公告)号：WO2015048103A1

公开(公告)日：2015-04-02

申请号：PCT/US2014/057175

申请日：2014-09-24

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： LIU, David ,  ZHOU, Shaohua Kevin ,  KRAMER, Martin ,  SUEHLING, Michael ,  TIETJEN, Christian ,  SOZA, Grzegorz ,  WIMMER, Andreas

IPC: G06T7/00 ,  G06T7/40 ,  G06K9/62 ,  A61B5/00

CPC classification number: A61B5/7267 ,  A61B5/055 ,  A61B5/4244 ,  A61B5/4848 ,  A61B5/7264 ,  A61B5/7275 ,  A61B5/742 ,  A61B6/032 ,  A61B6/5217 ,  A61B6/5229 ,  A61B2576/00 ,  G06K9/6214 ,  G06K9/624 ,  G06K9/6256 ,  G06K9/6259 ,  G06T7/0016 ,  G06T7/42 ,  G06T2207/10072 ,  G06T2207/10136 ,  G06T2207/20012 ,  G06T2207/20016 ,  G06T2207/20064 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20104 ,  G06T2207/30056 ,  G06T2207/30096

Abstract: For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient

Abstract translation: 对于治疗反应评估，纹理特征被输入用于机器学习分类器和使用机器学习分类器。 数据驱动的纹理特征不是使用基于公式的纹理特征，而是来自训练图像。 这样的数据驱动纹理特征是独立的分析特征，例如来自独立子空间分析的特征。 纹理特征可以用于基于对患者的几次甚至一次扫描来预测治疗结果

公开(公告)号：WO2012040410A9

公开(公告)日：2012-03-29

申请号：PCT/US2011/052675

申请日：2011-09-22

Applicant: SIEMENS CORPORATION ,  SIEMENS AKTIENGESELLSCHAFT ,  LIU, David ,  WU, Dijia ,  ZHOU, Shaohua Kevin ,  COSTA, Maria Jimena ,  SUEHLING, Michael ,  TIETJEN, Christian

Inventor： LIU, David ,  WU, Dijia ,  ZHOU, Shaohua Kevin ,  COSTA, Maria Jimena ,  SUEHLING, Michael ,  TIETJEN, Christian

IPC: G06T7/00

Abstract: A method and system for automatically detecting liver lesions in medical image data, such as 3D CT images, is disclosed. A liver region is segmented in a 3D image. Liver lesion center candidates are detected in the segmented liver region. Lesion candidates are segmented corresponding to the liver lesion center candidates, and lesions are detected from the segmented lesion candidates using learning based verification.

公开(公告)号：WO2016182551A1

公开(公告)日：2016-11-17

申请号：PCT/US2015/030084

申请日：2015-05-11

Applicant: SIEMENS HEALTHCARE GMBH ,  SIEMENS CORPORATION

Inventor： LIU, David ,  GEORGESCU, Bogdan ,  ZHENG, Yefeng ,  NGUYEN, Hien ,  ZHOU, Shaohua Kevin ,  SINGH, Vivek Kumar ,  COMANICIU, Dorin

IPC: G06K9/46 ,  G06K9/62

CPC classification number: G06K9/4628 ,  A61B8/52 ,  G06K9/627 ,  G06K2209/05 ,  G06T7/0012

Abstract: A method and system for anatomical landmark detection in medical images using deep neural networks is disclosed. For each of a plurality of image patches centered at a respective one of a plurality of voxels in the medical image, a subset of voxels within the image patch is input to a trained deep neural network based on a predetermined sampling pattern. A location of a target landmark in the medical image is detected using the trained deep neural network based on the subset of voxels input to the trained deep neural network from each of the plurality of image patches.

Abstract translation: 公开了一种使用深层神经网络的医学图像中解剖学标记检测的方法和系统。 对于以医学图像中的多个体素的相应一个为中心的多个图像补丁中的每一个，基于预定的采样模式，将图像补片内的体素子集输入训练的深层神经网络。 使用经训练的深层神经网络，基于从多个图像块中的每一个输入到经过训练的深层神经网络的体素子集来检测医学图像中的目标地标的位置。