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    Automatic cardiac view classification of echocardiography
    31.
    发明授权
    Automatic cardiac view classification of echocardiography 有权
    自动心脏超声心动图分类

    公开(公告)号：US08170303B2

    公开(公告)日：2012-05-01

    申请号：US11775538

    申请日：2007-07-10

    Applicant: Shaohua Kevin Zhou ,  Jin-hyeong Park ,  Joanne Otsuki ,  Dorin Comaniciu ,  Bogdan Georgescu ,  Constantine Simopoulos

    Inventor： Shaohua Kevin Zhou ,  Jin-hyeong Park ,  Joanne Otsuki ,  Dorin Comaniciu ,  Bogdan Georgescu ,  Constantine Simopoulos

    IPC: G06K9/00

    CPC classification number: G06K9/6256 ,  G06K9/6292 ,  G06K2209/051

    Abstract: A method for view classification includes providing a frame of an object of interest, detecting a region of interest within the object of interest for each of a plurality of detectors (e.g., binary classifiers), wherein each binary classifier corresponds to a different view, performing a global view classification using a multiview classifier for each view, outputting a classification for each view, fusing outputs of the multiview classifiers, and determining and outputting a classification of the frame based on a fused output of the multiview classifiers.

    Abstract translation: 一种用于视图分类的方法包括提供感兴趣对象的帧，检测多个检测器（例如，二进制分类器）中的每一个的感兴趣对象内的感兴趣区域，其中每个二进制分类器对应于不同视图，执行 对于每个视图使用多视角分类器的全局视图分类，输出每个视图的分类，融合多视角分类器的输出，以及基于多视角分类器的融合输出来确定和输出该帧的分类。

    COMPUTERIZED CHARACTERIZATION OF CARDIAC MOTION IN MEDICAL DIAGNOSTIC ULTRASOUND
    32.
    发明申请
    COMPUTERIZED CHARACTERIZATION OF CARDIAC MOTION IN MEDICAL DIAGNOSTIC ULTRASOUND 审中-公开
    医学诊断超声心脏运动的计算机表征

    公开(公告)号：US20120078097A1

    公开(公告)日：2012-03-29

    申请号：US13234697

    申请日：2011-09-16

    Applicant: Yang Wang ,  Bogdan Georgescu ,  Helene C. Houle ,  Dorin Comaniciu

    Inventor： Yang Wang ,  Bogdan Georgescu ,  Helene C. Houle ,  Dorin Comaniciu

    IPC: A61B8/08

    CPC classification number: A61B8/483 ,  A61B8/00 ,  A61B8/0883 ,  G06T7/251 ,  G06T2207/10136 ,  G06T2207/30048

    Abstract: Computerized characterization of cardiac wall motion is provided. Quantities for cardiac wall motion are determined from a four-dimensional (i.e., 3D+time) sequence of ultrasound data. A processor automatically processes the volume data to locate the cardiac wall through the sequence and calculate the quantity from the cardiac wall position or motion. Various machine learning is used for locating and tracking the cardiac wall, such as using a motion prior learned from training data for initially locating the cardiac wall and the motion prior, speckle tracking, boundary detection, and mass conservation cues for tracking with another machine learned classifier. Where the sequence extends over multiple cycles, the cycles are automatically divided for independent tracking of the cardiac wall. The cardiac wall from one cycle may be used to propagate to another cycle for initializing the tracking. Independent tracking in each cycle may reduce or avoid inaccuracies due to drift.

    Abstract translation: 提供了心脏壁运动的计算机化特征。 用于心脏壁运动的量由超声数据的四维（即3D +时间）序列确定。 处理器自动处理体积数据以通过序列定位心脏壁，并从心脏壁位置或运动计算量。 使用各种机器学习来定位和跟踪心脏壁，例如使用从训练数据学习的运动，用于初始定位心脏壁和运动，斑点跟踪，边界检测和用于跟踪另一机器学习的质量保护线索 分类器 当序列延伸多个周期时，循环被自动划分为心脏壁的独立跟踪。 来自一个周期的心脏壁可用于传播到另一周期以初始化跟踪。 每个循环中的独立跟踪可能会减少或避免由于漂移引起的不准确。

    Method and System for Image Based Device Tracking for Co-registration of Angiography and Intravascular Ultrasound Images
    34.
    发明申请
    Method and System for Image Based Device Tracking for Co-registration of Angiography and Intravascular Ultrasound Images 有权
    用于血管造影和血管内超声图像共同配准的基于图像的设备跟踪的方法和系统

    公开(公告)号：US20120059253A1

    公开(公告)日：2012-03-08

    申请号：US13171560

    申请日：2011-06-29

    Applicant: Peng Wang ,  Simone Prummer ,  Terrence Chen ,  Dorin Comaniciu ,  Olivier Ecabert ,  Martin Ostermeier

    Inventor： Peng Wang ,  Simone Prummer ,  Terrence Chen ,  Dorin Comaniciu ,  Olivier Ecabert ,  Martin Ostermeier

    IPC: A61B6/00 ,  A61B8/00

    CPC classification number: A61B8/0891 ,  A61B6/00 ,  A61B6/12 ,  A61B6/481 ,  A61B6/487 ,  A61B6/504 ,  A61B6/5247 ,  A61B8/12 ,  A61B8/5238 ,  G06T7/251 ,  G06T2207/10121 ,  G06T2207/10132

    Abstract: A method and system for co-registration of angiography data and intra vascular ultrasound (IVUS) data is disclosed. A vessel branch is detected in an angiogram image. A sequence of IVUS images is received from an IVUS transducer while the IVUS transducer is being pulled back through the vessel branch. A fluoroscopic image sequence is received while the IVUS transducer is being pulled back through the vessel branch. The IVUS transducer and a guiding catheter tip are detected in each frame of the fluoroscopic image sequence. The IVUS transducer detected in each frame of the fluoroscopic image sequence is mapped to a respective location in the detected vessel branch of the angiogram image. Each of the IVUS images is registered to a respective location in the detected vessel branch of the angiogram image based on the mapped location of the IVUS transducer detected in a corresponding frame of the fluoroscopic image sequence.

    Abstract translation: 公开了用于血管造影数据和血管内超声（IVUS）数据共同配准的方法和系统。 在血管造影图像中检测血管分支。 当IVUS传感器被拉回血管分支时，从IVUS换能器接收一系列IVUS图像。 当IVUS传感器被拉回通过血管分支时，接收透视图像序列。 在荧光镜图像序列的每个帧中检测IVUS换能器和引导导管尖端。 在荧光镜像图像序列的每个帧中检测到的IVUS换能器被映射到血管造影图像的检测到的血管分支中的相应位置。 基于在荧光镜像图像序列的相应帧中检测到的IVUS换能器的映射位置，将每个IVUS图像登记在血管造影图像的检测的血管分支中的相应位置。

    Method and system for detecting 3D anatomical structures using constrained marginal space learning
    35.
    发明授权
    Method and system for detecting 3D anatomical structures using constrained marginal space learning 有权
    使用约束边际空间学习检测3D解剖结构的方法和系统

    公开(公告)号：US08116548B2

    公开(公告)日：2012-02-14

    申请号：US12471761

    申请日：2009-05-26

    Applicant: Yefeng Zheng ,  Bogdan Georgescu ,  Haibin Ling ,  Michael Scheuering ,  Dorin Comaniciu

    Inventor： Yefeng Zheng ,  Bogdan Georgescu ,  Haibin Ling ,  Michael Scheuering ,  Dorin Comaniciu

    IPC: G06K9/00

    CPC classification number: G06K9/3233 ,  G06K2209/051 ,  G06T7/75 ,  G06T2207/10081 ,  G06T2207/30004

    Abstract: A method and apparatus for detecting 3D anatomical objects in medical images using constrained marginal space learning (MSL) is disclosed. A constrained search range is determined for an input medical image volume based on training data. A first trained classifier is used to detect position candidates in the constrained search range. Position-orientation hypotheses are generated from the position candidates using orientation examples in the training data. A second trained classifier is used to detect position-orientation candidates from the position-orientation hypotheses. Similarity transformation hypotheses are generated from the position-orientation candidates based on scale examples in the training data. A third trained classifier is used to detect similarity transformation candidates from the similarity transformation hypotheses, and the similarity transformation candidates define the position, translation, and scale of the 3D anatomic object in the medical image volume.

    Abstract translation: 公开了一种使用受限边际空间学习（MSL）检测医学图像中3D解剖学对象的方法和装置。 基于训练数据确定输入医学图像体积的约束搜索范围。 第一训练分类器用于检测约束搜索范围内的位置候选。 使用训练数据中的取向示例从位置候选者生成位置取向假设。 第二训练分类器用于从位置定向假设检测位置方向候选。 基于训练数据中的比例示例，从位置定位候选生成相似度转换假设。 第三训练分类器用于从相似变换假设检测相似变换候选，并且相似变换候选定义医学图像体积中的3D解剖对象的位置，平移和比例。

    Method and system for measuring left ventricle volume
    36.
    发明授权
    Method and system for measuring left ventricle volume 有权
    测量左心室体积的方法和系统

    公开(公告)号：US08098918B2

    公开(公告)日：2012-01-17

    申请号：US12228911

    申请日：2008-08-18

    Applicant: Yefeng Zheng ,  Bogdan Georgescu ,  Michael Scheuering ,  Dorin Comaniciu

    Inventor： Yefeng Zheng ,  Bogdan Georgescu ,  Michael Scheuering ,  Dorin Comaniciu

    IPC: G06K9/00

    CPC classification number: G06T7/62 ,  G06T7/11 ,  G06T2207/10081 ,  G06T2207/30048

    Abstract: A method and system for measuring the volume of the left ventricle (LV) in a 3D medical image, such as a CT, volume is disclosed. Heart chambers are segmented in the CT volume, including at least the LV endocardium and the LV epicardium. An optimal threshold value is automatically determined based on voxel intensities within the LV endocardium and voxel intensities between the LV endocardium and the LV epicardium. Voxels within the LV endocardium are labeled as blood pool voxels or papillary muscle voxels based on the optimal threshold value. The LV volume can be measured excluding the papillary muscles based on the number of blood pool voxels, and the LV volume can be measured including the papillary muscles based on the total number of voxels within the LV endocardium.

    Abstract translation: 公开了一种用于测量3D医学图像（例如CT）体积中的左心室（LV）的体积的方法和系统。 心室在CT体积中分段，至少包括LV心内膜和LV心外膜。 基于LV心内膜内的体素强度和LV心内膜与LV心外膜之间的体素强度自动确定最佳阈值。 基于最佳阈值将LV心内膜内的体素标记为血池体素或乳头肌肉体素。 基于血液池体素的数量可以除去乳头肌之外的LV体积，并且可以基于LV心内膜中的体素总数来测量LV体积，包括乳头肌。

    CARDIAC FLOW QUANTIFICATION WITH VOLUMETRIC IMAGING DATA
    37.
    发明申请
    CARDIAC FLOW QUANTIFICATION WITH VOLUMETRIC IMAGING DATA 有权
    具有体积成像数据的心脏流量定量

    公开(公告)号：US20110301466A1

    公开(公告)日：2011-12-08

    申请号：US13151803

    申请日：2011-06-02

    Applicant: Yang Wang ,  Bogdan Georgescu ,  Saurabh Datta ,  Dorin Comaniciu

    Inventor： Yang Wang ,  Bogdan Georgescu ,  Saurabh Datta ,  Dorin Comaniciu

    IPC: A61B8/06

    CPC classification number: A61B8/065 ,  A61B6/503 ,  A61B8/06 ,  A61B8/0883 ,  A61B8/13 ,  G01S15/584 ,  G01S15/8979 ,  G06T5/002 ,  G06T7/0016 ,  G06T2207/10136 ,  G06T2207/20182 ,  G06T2207/30048 ,  G06T2207/30104

    Abstract: A method quantifies cardiac volume flow for an imaging sequence. The method includes receiving data representing three-dimensions and color Doppler flow data over a plurality of frames, constructing a ventricular model based on the data representing three-dimensions for the plurality of frames, the ventricular model including a sampling plane configured to measure the cardiac volume flow, computing volume flow samples based on the sampling plane and the color Doppler flow data, and correcting the volume flow samples for aliasing based on volumetric change in the ventricular model between successive frames of the plurality of frames.

    Abstract translation: 一种方法量化成像序列的心脏体积流量。 该方法包括在多个帧上接收表示三维和彩色多普勒流数据的数据，基于代表多个帧的三维的数据构建心室模型，心室模型包括被配置成测量心脏的采样平面 体积流量，基于采样平面和彩色多普勒流数据的计算体积流量样本，以及基于多个帧的连续帧之间的心室模型的体积变化来校正用于混叠的体积流量样本。

    Fast detection of left ventricle and its configuration in 2D/3D echocardiogram using probabilistic boosting network
    39.
    发明授权
    Fast detection of left ventricle and its configuration in 2D/3D echocardiogram using probabilistic boosting network 有权
    使用概率增强网络快速检测左心室及其在2D / 3D超声心动图中的配置

    公开(公告)号：US08000497B2

    公开(公告)日：2011-08-16

    申请号：US11872972

    申请日：2007-10-16

    Applicant: Shaohua Kevin Zhou ,  Jingdan Zhang ,  Bogdan Georgescu ,  Dorin Comaniciu

    Inventor： Shaohua Kevin Zhou ,  Jingdan Zhang ,  Bogdan Georgescu ,  Dorin Comaniciu

    IPC: G06K9/00 ,  G06K9/62

    CPC classification number: G06K9/6256 ,  G06K9/00201 ,  G06K9/3233 ,  G06K2209/05 ,  G06T7/73 ,  G06T2207/30048

    Abstract: A method for detecting an object of interest in an input image includes the computer-implemented steps of: receiving an image, providing a multi-class pose classifier that identifies a plurality of pose features for estimating a pose of the object of interest, providing a plurality of cascades of serially-linked binary object feature classifiers, each cascade corresponding to different poses of the object of interest in the input image, selecting at least one of the cascades using the estimated pose, and employing the selected cascades to detect instances of the object of interest in the image.

    Abstract translation: 一种用于在输入图像中检测感兴趣对象的方法包括以下计算机实现的步骤：接收图像，提供识别用于估计感兴趣对象的姿态的多个姿势特征的多类姿势分类器，提供 多个级联的串联二进制对象特征分类器，每个级联对应于输入图像中感兴趣对象的不同姿态，使用估计姿态选择级联中的至少一个，并且采用所选择的级联来检测 图像中感兴趣的对象。

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