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    METHOD AND SYSTEM FOR CALCULATING RESECTED TISSUE VOLUME FROM 2D/2.5D INTRAOPERATIVE IMAGE DATA
    1.
    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

    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。

    SYSTEM AND METHOD FOR GUIDANCE OF LAPAROSCOPIC SURGICAL PROCEDURES THROUGH ANATOMICAL MODEL AUGMENTATION
    7.
    发明申请
    SYSTEM AND METHOD FOR GUIDANCE OF LAPAROSCOPIC SURGICAL PROCEDURES THROUGH ANATOMICAL MODEL AUGMENTATION 审中-公开
    通过解剖学模型建立指导手术方法的系统和方法

    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。

    LEVERAGING ON LOCAL AND GLOBAL TEXTURES OF BRAIN TISSUES FOR ROBUST AUTOMATIC BRAIN TUMOR DETECTION
    8.
    发明申请
    LEVERAGING ON LOCAL AND GLOBAL TEXTURES OF BRAIN TISSUES FOR ROBUST AUTOMATIC BRAIN TUMOR DETECTION 审中-公开
    利用脑组织的局部和全局纹理进行稳健的自动脑肿瘤检测

    公开(公告)号：WO2017151307A1

    公开(公告)日：2017-09-08

    申请号：PCT/US2017/017798

    申请日：2017-02-14

    Applicant: SIEMENS AKTIENGESELLSCHAFT

    Inventor： BHATTACHARYA, Subhabrata ,  CHEN, Terrence ,  KAMEN, Ali ,  SUN, Shanhui

    IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62

    CPC classification number: G06T7/0012 ,  A61B1/00045 ,  A61B1/04 ,  A61B5/4064 ,  A61B5/7282 ,  G06K9/00147 ,  G06K9/4642 ,  G06K9/4671 ,  G06K9/4676 ,  G06K9/52 ,  G06K9/6218 ,  G06K9/6262 ,  G06K9/6267 ,  G06K9/6269 ,  G06K9/6282 ,  G06K9/6292 ,  G06K2009/4666 ,  G06T7/41 ,  G06T2207/30016 ,  H04N19/124 ,  H04N19/136

    Abstract: A method for performing cellular classification includes generating a plurality of local dense Scale Invariant Feature Transform (SIFT) features based on a set of input images and converting the plurality of local dense SIFT features into a multi-dimensional code using a feature coding process. A first classification component is used to generate first output confidence values based on the multi-dimensional code and a plurality of global Local Binary Pattern Histogram (LBP-H) features are generated based on the set of input images. A second classification component is used to generate second output confidence values based on the plurality of LBP-H features and the first output confidence values and the second output confidence values are merged. Each of the set of input images may then be classified as one of a plurality of cell types using the merged output confidence values.

    Abstract translation: 用于执行细胞分类的方法包括基于一组输入图像生成多个局部密集尺度不变特征变换（SIFT）特征并且将所述多个局部密集SIFT特征转换成多维 代码使用特征编码过程。 第一分类组件用于基于多维代码生成第一输出置信度值，并且基于该组输入图像生成多个全局局部二值模式直方图（LBP-H）特征。 第二分类组件用于基于多个LBP-H特征生成第二输出置信度值，并且合并第一输出置信度值和第二输出置信度值。 然后可以使用合并输出置信度值将该组输入图像中的每一个分类为多个单元类型中的一个。

    METHOD AND SYSTEM FOR SIMULTANEOUS SCENE PARSING AND MODEL FUSION FOR ENDOSCOPIC AND LAPAROSCOPIC NAVIGATION
    9.
    发明申请
    METHOD AND SYSTEM FOR SIMULTANEOUS SCENE PARSING AND MODEL FUSION FOR ENDOSCOPIC AND LAPAROSCOPIC NAVIGATION 审中-公开
    用于内窥镜和腹腔镜导航的同时场景分割和模型融合的方法和系统

    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。

    METHOD AND SYSTEM FOR SEMANTIC SEGMENTATION IN LAPAROSCOPIC AND ENDOSCOPIC 2D/2.5D IMAGE DATA
    10.
    发明申请
    METHOD AND SYSTEM FOR SEMANTIC SEGMENTATION IN LAPAROSCOPIC AND ENDOSCOPIC 2D/2.5D IMAGE DATA 审中-公开
    LAPAROSCOPIC和ENDOSCOPIC 2D / 2.5D图像数据中的语义分割方法与系统

    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。

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