-
公开(公告)号：WO2017050861A1
公开(公告)日：2017-03-30
申请号：PCT/EP2016/072491
申请日：2016-09-22
Applicant: SIEMENS HEALTHCARE GMBH , SIEMENS AKTIENGESELLSCHAFT
Inventor： EL-ZEHIRY, Noha Youssry , GEORGESCU, Bogdan , HAYDEN, Oliver , KAMEN, Ali , PHILIPPI, Uwe , RAPAKA, Saikiran , RICHTER, Lukas , SCHICK, Anton , UGELE, Matthias
CPC classification number: G06T7/55 , G01N15/1475 , G01N2015/1006 , G01N2015/1454 , G03H1/0443 , G03H1/0866 , G03H2001/0883 , G06K9/0014 , G06K2209/403 , G06T2207/10056 , G06T2207/30024
Abstract: A computer-implemented method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system. The DHM image comprises depictions of one or more cell objects and background. A reference image is generated based on the DHM image. This reference image may then be used to reconstruct a fringe pattern in the DHM image into an optical depth map.
Abstract translation: 用于分析用于血液学应用的数字全息显微镜（DHM）数据的计算机实现的方法包括接收使用数字全息显微镜系统获取的DHM图像。 DHM图像包括描绘一个或多个单元对象和背景。 基于DHM图像生成参考图像。 然后可以使用该参考图像将DHM图像中的条纹图案重建成光学深度图。
-
2.发明申请
公开(公告)号：WO2019034328A1
公开(公告)日：2019-02-21
申请号：PCT/EP2018/068345
申请日：2018-07-06
Applicant: SIEMENS HEALTHCARE GMBH
Inventor： EL-ZEHIRY, Noha Youssry , KAMEN, Ali , RAPAKA, Saikiran
Abstract: A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.
-
公开(公告)号：WO2019238804A1
公开(公告)日：2019-12-19
申请号：PCT/EP2019/065447
申请日：2019-06-13
Applicant: SIEMENS HEALTHCARE GMBH
Inventor： ABDELRAHMAN, Kareem , COMANICIU, Dorin , DIALLO, Mamadou , GRIMM, Robert , KAMEN, Ali , KIEFER, Berthold , LOU, Bin , MENG, Tongbai , SHI, Bibo , TUYSUZOGLU, Ahmet , VON RODEN, Nicolas , WINKEL, David Jean , YU, Xin , VON BUSCH, Heinrich
Abstract: Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.
-
4.发明申请HIGH ACCURACY 5-PART DIFFERENTIAL WITH DIGITAL HOLOGRAPHIC MICROSCOPY AND UNTOUCHED LEUKOCYTES FROM PERIPHERAL BLOOD 审中-公开具有数字全息显微镜和周围血细胞未受影响的白细胞的高精度5分微分
公开(公告)号：WO2017157555A1
公开(公告)日：2017-09-21
申请号：PCT/EP2017/051656
申请日：2017-01-26
Applicant: SIEMENS HEALTHCARE GMBH
Inventor： EL-ZEHIRY, Noha Youssry , HAYDEN, Oliver , KAMEN, Ali , RICHTER, Lukas , STANZEL, Manfred , UGELE, Matthias , KÜHN, Daniela , MARQUARDT, Gaby , SCHMIDT, Oliver
CPC classification number: G01N15/1434 , G01N15/1429 , G01N15/147 , G01N2015/1006 , G01N2015/1454 , G03H1/0005 , G03H1/0443 , G03H2001/005 , G03H2001/0445 , G03H2210/20 , G03H2210/55 , G03H2240/56
Abstract: The present invention relates to an improved method for marker-free detection of a cell type of at least one cell in a medium using microfluidics and digital holographic microscopy, as well as a device, particular for carrying out the method.
Abstract translation: 本发明涉及使用微流体学和数字全息显微镜在介质中对至少一种细胞的细胞类型进行无标记检测的改进方法，以及特别用于实施 该方法。
-
5.发明申请METHOD AND SYSTEM FOR CLASSIFICATION OF ENDOSCOPIC IMAGES USING DEEP DECISION NETWORKS 审中-公开使用深层决策网络对内窥镜图像进行分类的方法和系统
公开(公告)号：WO2017055412A1
公开(公告)日：2017-04-06
申请号：PCT/EP2016/073209
申请日：2016-09-29
Applicant: SIEMENS HEALTHCARE GMBH , SIEMENS CORPORATION
Inventor： BHATTACHARYA, Subhabrata , CHEN, Terrence , COMANICIU, Dorin , GEORGESCU, Bogdan , KAMEN, Ali , MA, Kai , N. MURTHY, Venkatesh , SINGH, Vivek Kumar , SUN, Shanhui
CPC classification number: G06K9/00147 , G06K9/4628 , G06K2209/05 , G06T7/0012 , G06T2207/10068 , G06T2207/20084 , G06T2207/30096
Abstract: A method and system for classification of endoscopic images is disclosed. An initial trained deep network classifier is used to classify endoscopic images and determine confidence scores for the endoscopic images. The confidence score for each endoscopic image classified by the initial trained deep network classifier is compared to a learned confidence threshold. For endoscopic images with confidence scores higher than the learned threshold value, the classification result from the initial trained deep network classifier is output. Endoscopic images with confidence scores lower than the learned confidence threshold are classified using a first specialized network classifier built on a feature space of the initial trained deep network classifier.
Abstract translation: 公开了一种用于内窥镜图像分类的方法和系统。 使用初始训练的深层网络分类器对内窥镜图像进行分类，并确定内窥镜图像的置信度得分。 将由初始训练的深层网络分类器分类的每个内窥镜图像的置信度得分与学习的置信阈值进行比较。 对于具有高于学习阈值的置信度分数的内窥镜图像，输出来自初始训练的深层网络分类器的分类结果。 使用建立在初始训练的深层网络分类器的特征空间上的第一专门网络分类器来分类低于学习的置信度阈值的内窥镜图像。
-
Patent Agency Ranking6.发明申请METHOD AND SYSTEM FOR CALCULATING RESECTED TISSUE VOLUME FROM 2D/2.5D INTRAOPERATIVE IMAGE DATA 审中-公开用于从2D / 2.5D非操作性图像数据计算预留的组织体积的方法和系统
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
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。
-
7.发明申请METHOD AND SYSTEM FOR MACHINE LEARNING BASED ASSESSMENT OF FRACTIONAL FLOW RESERVE 审中-公开用于机器学习的方法和系统基于流量保留的评估
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
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
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：WO2017222673A1
公开(公告)日：2017-12-28
申请号：PCT/US2017/032647
申请日：2017-05-15
Applicant: SIEMENS AKTIENGESELLSCHAFT
Inventor： KAMEN, Ali , KIRALY, Atilla Peter , PHEIFFER, Thomas , SCHICK, Anton
CPC classification number: H04N5/372 , A61B1/00006 , A61B1/043 , A61B1/045 , A61B1/05 , A61B1/06 , A61B1/0676 , G06T7/11 , G06T7/521 , G06T2207/10028 , G06T2207/10068 , G06T2207/10152 , G06T2207/30004 , H04N5/2256
Abstract: A projector in an endoscope is used to project visible light onto tissue. The projected intensity, color, and/or wavelength vary by spatial location in the field of view to provide an overlay. Rather than relying on a rendered overlay alpha-blended on a captured image, the illumination with spatial variation physically highlights one or more regions of interest or physically overlays on the tissue.
Abstract translation: 内窥镜中的投影仪用于将可见光投射到组织上。 投影强度，颜色和/或波长随视场中的空间位置而变化以提供覆盖。 而不是依赖渲染的叠加图像混合在捕获的图像上，具有空间变化的照明在物理上突出显示一个或多个感兴趣区域或物理叠加在组织上。
-
10.发明申请METHOD AND SYSTEM FOR REGISTRATION OF 2D/2.5D LAPAROSCOPIC AND ENDOSCOPIC IMAGE DATA TO 3D VOLUMETRIC IMAGE DATA 审中-公开2D / 2.5D LAPAROSCOPIC和内窥镜图像数据注册到3D体积图像数据的方法和系统
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。
-
-
-
-
-
-
-
-
-
Search Results
29
records
(took 0.014 seconds)
