公开(公告)号：WO2017066378A1

公开(公告)日：2017-04-20

申请号：PCT/US2016/056734

申请日：2016-10-13

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： KAMEN, Ali ,  KLUCKNER, Stefan ,  PHEIFFER, Thomas

IPC: G06T7/60 ,  G06T7/00

CPC classification number: A61B1/00009 ,  A61B1/04 ,  A61B34/30 ,  G06T7/55 ,  G06T7/602 ,  G06T7/62 ,  G06T17/10 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/10068 ,  G06T2207/30024 ,  G06T2207/30096

Abstract: A method and system for calculating a volume of resected tissue from a stream of intraoperative images is disclosed. A stream of 2D/2.5D intraoperative images of resected tissue of a patient is received. The 2D/2.5D intraoperative images in the stream are acquired at different angles with respect to the resected tissue. A resected tissue surface is segmented in each of the 2D/2.5D intraoperative images. The segmented resected tissue surfaces are stitched to generate a 3D point cloud representation of the resected tissue surface. A 3D mesh representation of the resected tissue surface is generated from the 3D point cloud representation of the resected tissue surface. The volume of the resected tissue is calculated from the 3D mesh representation of the resected tissue surface.

Abstract translation: 公开了一种用于从手术中图像流计算切除组织的体积的方法和系统。 接收患者的切除组织的2D / 2.5D术中图像流。 流中的2D / 2.5D术中图像相对于切除的组织以不同的角度采集。 切除的组织表面在每个2D / 2.5D术中图像中被分割。 分割的切除的组织表面被缝合以生成切除的组织表面的3D点云表示。 从切除的组织表面的3D点云表示生成切除的组织表面的3D网格表示。 根据切除组织表面的3D网格表示计算切除组织的体积。

公开(公告)号：WO2015058044A1

公开(公告)日：2015-04-23

申请号：PCT/US2014/061063

申请日：2014-10-17

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： SHARMA, Puneet ,  KAMEN, Ali ,  GEORGESCU, Bogdan ,  SAUER, Frank ,  COMANICIU, Dorin ,  ZHENG, Yefeng ,  NGUYEN, Hien ,  SINGH, Vivek Kumar

IPC: A61B6/00 ,  G06F19/00 ,  G06T7/00 ,  G06T7/20

CPC classification number: A61B5/026 ,  A61B5/0261 ,  A61B5/0263 ,  A61B5/7264 ,  A61B5/7267 ,  A61B5/7282 ,  A61B6/032 ,  A61B6/504 ,  A61B6/507 ,  A61B6/5217 ,  A61B6/563 ,  A61B8/06 ,  A61B8/0891 ,  A61B8/12 ,  A61B8/5223 ,  A61B8/565 ,  G06K9/46 ,  G06K9/6256 ,  G06T7/0012 ,  G06T7/0016 ,  G06T7/20 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10101 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30104 ,  G06T2211/404 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches

Abstract translation: 公开了一种用于确定患者冠状动脉狭窄的分数流量储备（FFR）的方法和系统。 在一个实施例中，接收包括狭窄的患者的医学图像数据，从患者的医学图像数据中提取用于狭窄的一组特征，并且基于所提取的特征集来确定狭窄的FFR值 使用训练有素的基于机器学习的映射。 在另一个实施例中，接收到包括感兴趣狭窄的患者的医学图像，检测到与感兴趣的狭窄相对应的图像斑块和患者的冠状动脉树，使用训练有素的深度来确定感兴趣狭窄的FFR值 神经网络回归器直接应用于检测到的图像补丁

公开(公告)号：WO2010132349A1

公开(公告)日：2010-11-18

申请号：PCT/US2010/034214

申请日：2010-05-10

Applicant: SIEMENS CORPORATION ,  SIEMENS AKTIENGESELLSCHAFT ,  KAMEN, Ali ,  WEIN, Wolfgang ,  KHURD, Parmeshwar ,  DIALLO, Mamadou ,  NANKE, Ralf ,  FEHRE, Jens ,  KIEFER, Berthold ,  REQUARDT, Martin ,  WEISS, Clifford

Inventor： KAMEN, Ali ,  WEIN, Wolfgang ,  KHURD, Parmeshwar ,  DIALLO, Mamadou ,  NANKE, Ralf ,  FEHRE, Jens ,  KIEFER, Berthold ,  REQUARDT, Martin ,  WEISS, Clifford

IPC: A61B10/02 ,  A61B19/00

CPC classification number: A61B10/0241 ,  A61B8/4254 ,  A61B34/20 ,  A61B2017/00274 ,  A61B2017/00694 ,  A61B2017/3413 ,  A61B2018/00547 ,  A61B2034/105 ,  A61B2034/107 ,  A61B2034/2051 ,  A61B2090/367 ,  A61B2090/374 ,  A61B2090/378

Abstract: In a method for image guided prostate cancer needle biopsy, a first registration is performed to match a first image of a prostate to a second image of the prostate (210). Third images of the prostate are acquired and compounded into a three-dimensional (3D) image (220). The prostate in the compounded 3D image is segmented to show its border (230). A second registration and then a third registration different from the second registration is performed on distance maps generated from the prostate borders of the first image and the compounded 3D image, wherein the first and second registrations are based on a biomechanical property of the prostate (240). A region of interest in the first image is mapped to the compounded 3D image or a fourth image of the prostate acquired with the second modality (250).

Abstract translation: 在用于图像引导的前列腺癌针活检的方法中，执行首先配准以将前列腺的第一图像与前列腺的第二图像（210）匹配。 获取前列腺的第三图像并将其复合成三维（3D）图像（220）。 复合3D图像中的前列腺被分割以显示其边界（230）。 在从第一图像和复合3D图像的前列腺边界生成的距离图上执行与第二登记不同的第二登记和第三登记，其中第一和第二登记基于前列腺的生物力学性质（240 ）。 将第一图像中的感兴趣区域映射到复合3D图像或用第二模态获取的前列腺的第四图像（250）。

公开(公告)号：WO2016182550A1

公开(公告)日：2016-11-17

申请号：PCT/US2015/030080

申请日：2015-05-11

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： PHEIFFER, Thomas ,  KLUCKNER, Stefan ,  MOUNTNEY, Peter ,  KAMEN, Ali

IPC: G06T7/00

CPC classification number: G06T3/0068 ,  A61B34/10 ,  A61B2034/105 ,  G06T7/0014 ,  G06T7/11 ,  G06T7/30 ,  G06T7/32 ,  G06T2200/04 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/10068 ,  G06T2207/10072 ,  G06T2207/20112 ,  G06T2207/30004 ,  G06T2207/30056

Abstract: A method and system for registration of 2D/2.5D laparoscopic or endoscopic image data to 3D volumetric image data is disclosed. A plurality of 2D/2.5D intra-operative images of a target organ are received, together with corresponding relative orientation measurements for the intraoperative images. A 3D medical image volume of the target organ is registered to the plurality of 2D/2.5D intra-operative images by calculating pose parameters to match simulated projection images of the 3D medical image volume to the plurality of 2D/2.5D intra-operative images, and the registration is constrained by the relative orientation measurements for the intra-operative images.

Abstract translation: 公开了将2D / 2.5D腹腔镜或内窥镜图像数据注册到3D体积图像数据的方法和系统。 接收目标器官的多个2D / 2.5D手术中图像以及对于术中图像的对应的相对取向测量。 通过计算姿势参数以将3D医学图像体积的模拟投影图像与多个2D / 2.5D手术内图像相匹配来将目标器官的3D医学图像体积登记到多个2D / 2.5D手术内图像 ，并且注册受到术中图像的相对取向测量的约束。

公开(公告)号：WO2016178690A1

公开(公告)日：2016-11-10

申请号：PCT/US2015/029680

申请日：2015-05-07

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： KAMEN, Ali ,  KLUCKNER, Stefan ,  CHANG, Yao-jen ,  MANSI, Tommaso ,  PASSERINI, Tiziano ,  CHEN, Terrance ,  MOUNTNEY, Peter ,  SCHICK, Anton

IPC: G06F19/00 ,  G06T7/00 ,  G06T17/00 ,  A61B5/00

CPC classification number: G06T7/344 ,  A61B5/00 ,  G06F19/00 ,  G06T15/04 ,  G06T17/20 ,  G06T2200/04 ,  G06T2200/08 ,  G06T2207/20076 ,  G06T2207/30056 ,  G06T2210/41 ,  G16H50/50

Abstract: Systems and methods for model augmentation include receiving intra-operative imaging data of an anatomical object of interest at a deformed state. The intraoperative imaging data is stitched into an intra-operative model of the anatomical object of interest at the deformed state. The intra-operative model of the anatomical object of interest at the deformed state is registered with a pre-operative model of the anatomical object of interest at an initial state by deforming the pre-operative model of the anatomical object of interest at the initial state based on a biomechanical model. Texture information from the intra-operative model of the anatomical object of interest at the deformed state is mapped to the deformed pre-operative model to generate a deformed, texture-mapped pre-operative model of the anatomical object of interest.

Abstract translation: 用于模型增强的系统和方法包括在变形状态下接收感兴趣的解剖学对象的术中成像数据。 将术中成像数据在变形状态下缝合到感兴趣的解剖学对象的手术中模型中。 在初始状态下，通过使感兴趣的解剖学对象的手术前模型在初始状态下变形来将在变形状态下感兴趣的解剖学对象的手术中模型与感兴趣的解剖学对象的术前模型对齐 基于生物力学模型。 在变形状态下来自感兴趣的解剖学对象的手术中模型的纹理信息被映射到变形的手术前模型，以产生感兴趣的解剖学对象的变形的纹理映射的手术前模型。

公开(公告)号：WO2016195698A1

公开(公告)日：2016-12-08

申请号：PCT/US2015/034327

申请日：2015-06-05

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： KLUCKNER, Stefan ,  KAMEN, Ali ,  CHEN, Terrence

IPC: G06K9/62 ,  G06K9/32 ,  G06K9/50 ,  G06T7/00

CPC classification number: G06T7/251 ,  G06K9/3233 ,  G06K9/50 ,  G06K9/6259 ,  G06K9/6282 ,  G06K2209/051 ,  G06T7/11 ,  G06T2200/04 ,  G06T2207/10016 ,  G06T2207/10068 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/30056

Abstract: A method and system for scene parsing and model fusion in laparoscopic and endoscopic 2D/2.5D image data is disclosed. A current frame of an intra-operative image stream including a 2D image channel and a 2.5D depth channel is received. A 3D pre-operative model of a target organ segmented in pre-operative 3D medical image data is fused to the current frame of the intra-operative image stream. Semantic label information is propagated from the pre-operative 3D medical image data to each of a plurality of pixels in the current frame of the intra-operative image stream based on the fused pre-operative 3D model of the target organ, resulting in a rendered label map for the current frame of the intra-operative image stream. A semantic classifier is trained based on the rendered label map for the current frame of the intra-operative image stream.

Abstract translation: 公开了一种用于腹腔镜和内窥镜2D / 2.5D图像数据的场景解析和模型融合的方法和系统。 接收包括2D图像信道和2.5D深度信道的手术内图像流的当前帧。 在术前3D医学图像数据中分割的靶器官的3D手术前模型与术中图像流的当前帧融合。 基于目标器官的融合的术前3D模型，语义标签信息从术前3D医学图像数据传播到术中图像流的当前帧中的多个像素中的每一个，导致呈现 术语图像流的当前帧的标签映射。 语义分类器是基于针对当前图像流的当前帧的渲染标签映射进行训练的。

公开(公告)号：WO2016175773A1

公开(公告)日：2016-11-03

申请号：PCT/US2015/028120

申请日：2015-04-29

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： KLUCKNER, Stefan ,  KAMEN, Ali ,  CHEN, Terrence

IPC: G06T7/00

CPC classification number: G06T7/11 ,  G06K9/3233 ,  G06T7/13 ,  G06T7/344 ,  G06T2207/10028 ,  G06T2207/10068 ,  G06T2207/30004

Abstract: A method and system for semantic segmentation laparoscopic and endoscopic 2D/2.5D image data is disclosed. Statistical image features that integrate a 2D image channel and a 2.5D depth channel of a 2D/2.5 laparoscopic or endoscopic image are extracted for each pixel in the image. Semantic segmentation of the laparoscopic or endoscopic image is then performed using a trained classifier to classify each pixel in the image with respect to a semantic object class of a target organ based on the extracted statistical image features. Segmented image masks resulting from the semantic segmentation of multiple frames of a laparoscopic or endoscopic image sequence can be used to guide organ specific 3D stitching of the frames to generate a 3D model of the target organ.

Abstract translation: 公开了一种语义分割方法和系统，用于腹腔镜和内窥镜2D / 2.5D图像数据。 为图像中的每个像素提取整合2D / 2.5腹腔镜或内窥镜图像的2D图像通道和2.5D深度通道的统计图像特征。 然后使用经过训练的分类器，基于所提取的统计图像特征，针对目标器官的语义对象类对图像中的每个像素进行分类，来执行腹腔镜或内窥镜图像的语义分割。 可以使用由腹腔镜或内窥镜图像序列的多个帧的语义分割产生的分割图像掩模来引导框架的器官特异性3D缝合以产生目标器官的3D模型。

公开(公告)号：WO2015126815A1

公开(公告)日：2015-08-27

申请号：PCT/US2015/016126

申请日：2015-02-17

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： PASSERINI, Tiziano ,  MANSI, Tommaso ,  KAMEN, Ali ,  GEORGESCU, Bogdan ,  RAPAKA, Saikiran ,  COMANICIU, Dorin

IPC: A61B19/00 ,  G06F17/00 ,  G06F17/50 ,  G06F19/00 ,  G06T7/00

CPC classification number: G06F19/3437 ,  A61B34/10 ,  A61B2034/105 ,  G06F17/11 ,  G06F17/5009 ,  G06F19/00 ,  G16H50/50

Abstract: A method and system for simulating patient-specific cardiac electrophysiology including the effect of the electrical conduction system of the heart is disclosed. A patient-specific anatomical heart model is generated from cardiac image data of a patient. The electrical conduction system of the heart of the patient is modeled by determining electrical diffusivity values of cardiac tissue based on a distance of the cardiac tissue from the endocardium. A distance field from the endocardium surface is calculated with sub-grid accuracy using a nested-level set approach. Cardiac electrophysiology for the patient is simulated using a cardiac electrophysiology model with the electrical diffusivity values determined to model the Purkinje network of the patient.

Abstract translation: 公开了一种用于模拟患者特异性心脏电生理学的方法和系统，包括心脏导电系统的作用。 从患者的心脏图像数据生成患者特异性解剖心脏模型。 通过基于心脏组织与心内膜的距离确定心脏组织的电扩散系数来建模患者心脏的导电系统。 使用嵌套级集合方法使用子网格精度计算来自心内膜表面的距离场。 使用心电生理学模型模拟患者的心脏电生理学，确定电扩散系数值以建模患者的浦肯野网络。

公开(公告)号：WO2014133924A1

公开(公告)日：2014-09-04

申请号：PCT/US2014/017886

申请日：2014-02-24

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  SIEMENS CORPORATION

Inventor： AUDIGIER, Chloe ,  MANSI, Tommaso ,  MIHALEF, Viorel ,  KAMEN, Ali ,  COMANICIU, Dorin ,  SHARMA, Puneet ,  RAPAKA, Saikiran

IPC: A61B19/00 ,  A61B18/12

CPC classification number: A61B19/50 ,  A61B6/032 ,  A61B6/50 ,  A61B6/5217 ,  A61B34/10 ,  A61B2018/00404 ,  A61B2018/00529 ,  A61B2034/104 ,  A61B2034/105 ,  A61B2034/107 ,  G06F19/00 ,  G09B5/02 ,  G09B23/30 ,  G09B23/303 ,  G16H50/50

Abstract: A method and system for interactive patient-specific simulation of liver tumor ablation is disclosed. A patient-specific anatomical model of the liver and circulatory system of the liver is estimated from 3D medical image data of a patient. A computational domain is generated from the patient-specific anatomical model of the liver. Blood flow in the liver and the circulatory system of the liver is simulated based on the patient-specific anatomical model. Heat diffusion due to ablation is simulated based on a virtual ablation probe position and the simulated blood flow in the liver and the circulatory system of the liver by solving a bio-heat equation for each node on the level-set representation using a Lattice-Boltzmann method (LBM) implementation. Cellular necrosis in the liver is computed based on the simulated heat diffusion. Visualizations of a computed necrosis region and temperature maps of the liver are generated. A user input is interactively received to modify the position of the virtual ablation probe, the heat diffusion and cellular necrosis is re-simulated based on the user input, and the visualizations of the computed necrosis region and the temperature maps are updated

Abstract translation: 公开了用于肝肿瘤消融的患者特异性模拟交互的方法和系统。 根据患者的3D医学图像数据估计肝脏和肝脏循环系统的患者特异性解剖模型。 从肝脏的患者特异性解剖模型中产生计算结构域。 根据患者特异性解剖模型模拟肝脏和肝脏循环系统的血流。 基于虚拟消融探针位置和肝脏和肝循环系统中的模拟血流模拟了消融引起的热扩散，通过使用莱迪思 - 玻尔兹曼（Lattice-Boltzmann）在水平仪表示上求解每个节点的生物热方程 方法（LBM）实现。 基于模拟的热扩散计算肝脏细胞坏死。 产生计算坏死区域和肝脏温度图的可视化。 交互式地接收用户输入以修改虚拟消融探针的位置，基于用户输入重新模拟热扩散和细胞坏死，并且计算的坏死区域和温度图的可视化被更新

公开(公告)号：WO2014008332A3

公开(公告)日：2014-01-09

申请号：PCT/US2013/049205

申请日：2013-07-03

Applicant: SIEMENS CORPORATION ,  SIEMENS AG OSTERREICH ,  SIEMENS AKTIENGESELLSCHAFT ,  MANSI, Tommaso ,  LIM, Wei, Keat ,  KING, Vanessa ,  KREMER, Andreas ,  GEORGESCU, Bogdan ,  ZHENG, Xudong ,  KAMEN, Ali ,  KELLER, Andreas ,  STAEHLER, Cord, Friedrich ,  WIRSZ, Emil ,  COMANICIU, Dorin

Inventor： MANSI, Tommaso ,  LIM, Wei, Keat ,  KING, Vanessa ,  KREMER, Andreas ,  GEORGESCU, Bogdan ,  ZHENG, Xudong ,  KAMEN, Ali ,  KELLER, Andreas ,  STAEHLER, Cord, Friedrich ,  WIRSZ, Emil ,  COMANICIU, Dorin

IPC: G06F19/12 ,  G06F19/00

Abstract: A system operating in a plurality of modes to provide an integrated analysis of molecular data, imaging data, and clinical data associated with a patient includes a multi-scale model, a molecular model, and a linking component. The multi-scale model is configured to generate one or more estimated multi-scale parameters based on the clinical data and the imaging data when the system operates in a first mode, and generate a model of organ functionality based on one or more inferred multi-scale parameters when the system operates in a second mode. The molecular model is configured to generate one or more first molecular findings based on a molecular network analysis of the molecular data, wherein the molecular model is constrained by the estimated parameters when the system operates in the first mode. The linking component, which is operably coupled to the multi-scale model and the molecular model, is configured to transfer the estimated multi-scale parameters from the multi-scale model to the molecular model when the system operates in the first mode, and generate, using a machine learning process, the inferred multi-scale parameters based on the molecular findings when the system operates in the second mode.