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    IDENTIFYING STENT DEFORMATIONS
    3.
    发明申请
    IDENTIFYING STENT DEFORMATIONS 审中-公开

    公开(公告)号：WO2022268974A1

    公开(公告)日：2022-12-29

    申请号：PCT/EP2022/067218

    申请日：2022-06-23

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： WISSEL, Tobias

    IPC: G06T7/00 ,  G06T7/60 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20084 ,  G06T2207/30101 ,  G06T7/0012

    Abstract: A system (100) for identifying deformations of a deployed stent, is provided. The system includes one or more processors (110) configured to: receive (SI 10) X-ray image data representing one or more X-ray images (120) of a deployed stent (130) within a lumen (140), the stent including a plurality of stent struts (150); analyse (S120) the X-ray image data to determine a distribution of the stent struts (150) along an axis (160) of the lumen (140); and identify (S130) one or more longitudinally-deformed portions (170, 180) of the stent based on a density of the determined distribution of the stent struts (150) along the axis (160) of the lumen (140).

    MEDICAL IMAGE ANALYSIS USING NEURAL NETWORKS
    4.
    发明申请
    MEDICAL IMAGE ANALYSIS USING NEURAL NETWORKS 审中-公开

    公开(公告)号：WO2022208060A2

    公开(公告)日：2022-10-06

    申请号：PCT/GB2022/050765

    申请日：2022-03-28

    Applicant: UCL BUSINESS LTD

    Inventor： LILAONITKUL, Watjana ,  DUBIS, Adam

    IPC: G06T7/12 ,  G06T7/00 ,  G06N3/04 ,  G06N3/08 ,  G06N3/0454 ,  G06T2200/04 ,  G06T2207/10068 ,  G06T2207/10081 ,  G06T2207/10101 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30028 ,  G06T2207/30041 ,  G06T2207/30088 ,  G06T7/0012 ,  G06T7/0014

    Abstract: Systems and methods are described for automatically determining layer structure from medical image data. A processing device receives image data of biological layers captured by a medical imaging device. The processing device determines a boundary surface score for each pixel of the image data using a neural network, the boundary surface score being representative of a likelihood that each pixel corresponds to a boundary between segmented layers within the image data, to generate data defining boundary surfaces between segmented layers in the image data. In one embodiment, the neural network includes first and second sub-networks connected in series, the first sub- network configured with a multi-scale pooling layer that provides additional filters at respective defined sampling rates. The first sub-network processes the image data to generate segmentation data identifying a plurality of tissue layers in the input medical image, and the second sub-network processes the segmentation data to identify boundary surfaces between the plurality of tissue layers. Other embodiments are also described and claimed.

    DEEP LEARNING CARDIAC SEGMENTATION AND MOTION VISUALIZATION
    6.
    发明申请
    DEEP LEARNING CARDIAC SEGMENTATION AND MOTION VISUALIZATION 审中-公开

    公开(公告)号：WO2022020394A1

    公开(公告)日：2022-01-27

    申请号：PCT/US2021/042438

    申请日：2021-07-20

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ,  RIGOLLI, Marzia

    Inventor： CONTIJOCH, Francisco ,  CHEN, Zhennong ,  VIGNEAULT, Davis

    IPC: G06T7/10 ,  G06N3/02 ,  G16H30/40 ,  G06N3/0454 ,  G06N3/084 ,  G06T2207/10081 ,  G06T2207/20084 ,  G06T2207/30048 ,  G06T7/11

    Abstract: Devices, systems, and methods for automated segmentation and slicing of cardiac computed tomography (CT) images are described. An example method includes receiving a first plurality of input image frames associated with a cardiac CT operation, each of the first plurality of input image frames comprising a representation of two or more chambers of a heart, and performing, using a convolutional neural network, a segmentation operation and a slicing operation on each of the first plurality of input image frames to generate each of a plurality of output image frames comprising results of the segmentation operation and the slicing operation, wherein the segmentation operation comprises identifying volumes of each of the two or more chambers of the heart based on blood volumes, wherein the slicing operation comprises identifying one or more features of the heart in at least one predefined plane in a coordinate system associated with the cardiac CT operation.

    基于CT序列图像获取主动脉中心线的方法和系统
    7.
    发明申请
    基于CT序列图像获取主动脉中心线的方法和系统 审中-公开

    公开(公告)号：WO2022000733A1

    公开(公告)日：2022-01-06

    申请号：PCT/CN2020/110230

    申请日：2020-08-20

    Applicant: 苏州润迈德医疗科技有限公司

    Inventor： 王之元 ,  冯亮 ,  刘广志 ,  陈韵岱

    IPC: G06T7/00 ,  G06T7/136 ,  G06T7/187 ,  G06T7/66 ,  G06T5/40 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/30012 ,  G06T2207/30048 ,  G06T7/0012

    Abstract: 本申请提供了一种基于CT序列图像获取主动脉中心线的方法和系统，方法包括：获取CT序列图像的三维数据；根据三维数据获取心脏重心和脊椎重心；从CT三维图像上过滤杂质数据，获得含有左心房、左心室、无干扰冠脉树的图像；分层切片，得到二值化图像组；从二值化图像组中的每层切片上获得圆心，圆的半径，生成点列表和半径列表；将位于点列表和半径列表中的像素点对应到图像中，得到主动脉中心线。本申请通过先筛选出心脏重心和脊椎重心，对心脏和脊椎的位置进行定位，然后根据心脏和脊椎的位置从CT图像上去除肺部组织、降主动脉、脊椎和肋骨，再对处理过的图像提取主动脉中心线，减少了运算量，算法简单，容易操作，运算速度快，设计科学，图像处理精准。

    METHOD FOR CALCULATING A SEVERITY INDEX IN TRAUMATIC FRACTURES AND CORRESPONDING SYSTEM IMPLEMENTING THE METHOD
    9.
    发明申请
    METHOD FOR CALCULATING A SEVERITY INDEX IN TRAUMATIC FRACTURES AND CORRESPONDING SYSTEM IMPLEMENTING THE METHOD 审中-公开

    公开(公告)号：WO2021250710A1

    公开(公告)日：2021-12-16

    申请号：PCT/IT2021/050173

    申请日：2021-06-08

    Applicant: E-LISA S.R.L.

    Inventor： FIORENTINO, Fabrizio ,  PIETROLUONGO, Livia Renata ,  RUSSO, Raffaele ,  RICCIO, Daniel ,  ROSSI, Silvia

    IPC: G06T7/00 ,  A61B5/00 ,  A61B5/103 ,  A61B34/10 ,  A61B2034/105 ,  A61B5/4504 ,  A61B5/4509 ,  G06T2207/10081 ,  G06T2207/30008 ,  G06T7/0012 ,  G06T7/136 ,  G06T7/62 ,  G06T7/75 ,  G16H50/30 ,  G16H50/50

    Abstract: The present invention relates to a method for calculating a severity index in fractures of a humerus of an individual, from a plurality of section images Sk of said humerus, where k=1,...,N with N which is a positive integer, captured by means of a diagnostic imaging technique, characterized in that it comprises the following steps: A. normalizing the grey levels of each section image Sk of said plurality of images Sk, so that each section image Sk has the same grey level; B. starting from said normalized images in said step A., segmenting each section image Sk, in a significative region, comprising at least one bone structure, wherein at each pixel p is assigned a value 1, and in a non significative region, wherein at each pixel p is assigned value 0, so that each section image Sk is a binary image; C. starting from said section images Sk segmented in said step B., identifying at least one connected component corresponding to said humerus, or a shoulder blade, in said at least one bone structure comprised in each segmented section image Sk, and labelling said at least one connected component with a respective label, so that said humerus is labelled with a first label and said shoulder blade is labelled with a second label which is different from said first label; D. identifying a plurality of fragments of the head of said humerus, labelling each fragment of said plurality of fragments with a respective label and generating a real model (0) of said humerus from said plurality of fragments; E. given a reference model (M) of the integral bone structure of said humerus, recomposing and aligning said plurality of fragments, identified in said step D., in said real model (0) according to said reference model (M); and F. calculating a severity index of said fracture of said humerus starting from said plurality of fragments recomposed and aligned in said step E. The present invention also relates to a system which implements said method.

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