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    System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans
    12.
    发明授权
    System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans 有权
    用于计算机断层扫描的小型肺结节计算机辅助诊断系统，方法和装置

    公开(公告)号：US07751607B2

    公开(公告)日：2010-07-06

    申请号：US12277877

    申请日：2008-11-25

    Applicant: Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henschke ,  Antoni Chan

    Inventor： Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henschke ,  Antoni Chan

    IPC: G06K9/00

    CPC classification number: G06T7/0012 ,  G06T3/0075 ,  G06T7/44 ,  G06T2207/10081 ,  G06T2207/30061

    Abstract: The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, the lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points. The invention also improves the consistency of nodule segmentations. This invention uses rigid-body registration, histogram-matching, and a rule-based adjustment system to remove missegmented voxels between two segmentations of the same nodule at different times.

    Abstract translation: 本发明是使用连续结节候选细化方法的多级检测算法。 检测算法涉及四个主要步骤。 首先，从全肺CT扫描分割肺部区域。 随后是从肺区识别结节候选位置的假设生成阶段。 在第三阶段，结节候选子图像通过条纹伪影去除过程。 然后使用逐渐增加的复杂度的一系列滤波器连续地细化结节候选物。 第一个过滤器使用附件区域信息从结节候选列表中移除血管和大血管分叉点。 第二个过滤器移除小分叉点。 本发明还提高了结节分离的一致性。 本发明使用刚体登记，直方图匹配和基于规则的调整系统，以在不同时间去除相同结节的两个分段之间的错误分割的体素。

    System, Method and Apparatus for Small Pulmonary Nodule Computer Aided Diagnosis from Computed Tomography Scans
    13.
    发明申请
    System, Method and Apparatus for Small Pulmonary Nodule Computer Aided Diagnosis from Computed Tomography Scans 有权
    系统，方法和装置的小型肺结节计算机辅助诊断从计算机断层扫描

    公开(公告)号：US20090080748A1

    公开(公告)日：2009-03-26

    申请号：US12277877

    申请日：2008-11-25

    Applicant: Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henschke ,  Antoni Chan

    Inventor： Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henschke ,  Antoni Chan

    IPC: G06K9/00

    CPC classification number: G06T7/0012 ,  G06T3/0075 ,  G06T7/44 ,  G06T2207/10081 ,  G06T2207/30061

    Abstract: The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, the lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points. The invention also improves the consistency of nodule segmentations. This invention uses rigid-body registration, histogram-matching, and a rule-based adjustment system to remove missegmented voxels between two segmentations of the same nodule at different times.

    Abstract translation: 本发明是使用连续结节候选细化方法的多级检测算法。 检测算法涉及四个主要步骤。 首先，从全肺CT扫描分割肺部区域。 随后是从肺区识别结节候选位置的假设生成阶段。 在第三阶段，结节候选子图像通过条纹伪影去除过程。 然后使用逐渐增加的复杂度的一系列滤波器连续地细化结节候选物。 第一个过滤器使用附件区域信息从结节候选列表中移除血管和大血管分叉点。 第二个过滤器移除小分叉点。 本发明还提高了结节分离的一致性。 本发明使用刚体登记，直方图匹配和基于规则的调整系统，以在不同时间去除相同结节的两个分段之间的错误分割的体素。

    System and method for conducting a clinical trial study
    14.
    发明申请
    System and method for conducting a clinical trial study 有权
    进行临床试验研究的系统和方法

    公开(公告)号：US20060026034A1

    公开(公告)日：2006-02-02

    申请号：US10901362

    申请日：2004-07-28

    Applicant: David Yankelevitz ,  Claudia Henschke ,  Anthony Reeves

    Inventor： David Yankelevitz ,  Claudia Henschke ,  Anthony Reeves

    IPC: G06Q50/00

    CPC classification number: G06Q50/22 ,  G06F19/00 ,  G06Q50/24 ,  G16H10/20

    Abstract: Described is a method and system for conducting a clinical trial. Medical data is obtained from a patient participating in the clinical trial. Then, the medical data and at least one identifier are transmitted, via a communications network, for storage at a remote server. The at least one identifier links the medical data to a record of the patient. Access to at least portions of the medical data is provided, via the communications network, to trial participants based on predefined clinical trial procedures. The remote server tracks accessing of the medical data by the trial participants and generation by the trial participants of work product responsive to the medical data.

    Abstract translation: 描述了进行临床试验的方法和系统。 从参与临床试验的患者获得医学数据。 然后，经由通信网络传送医疗数据和至少一个标识符，以便在远程服务器处存储。 至少一个标识符将医疗数据链接到患者的记录。 基于预定义的临床试验程序，通过通信网络向医学参与者提供至少部分医疗数据的访问。 远程服务器跟踪试用参与者对医疗数据的访问和响应于医疗数据的工作产品的试用参与者的生成。

    Method and Apparatus for Small Pulmonary Nodule Computer Aided Diagnosis from Computed Tomography Scans
    15.
    发明申请
    Method and Apparatus for Small Pulmonary Nodule Computer Aided Diagnosis from Computed Tomography Scans 有权
    用于计算机断层扫描的小型肺结节计算机辅助诊断的方法和装置

    公开(公告)号：US20100272341A1

    公开(公告)日：2010-10-28

    申请号：US12829717

    申请日：2010-07-02

    Applicant: Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henschke ,  Antoni Chan

    Inventor： Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henschke ,  Antoni Chan

    IPC: G06K9/00

    CPC classification number: G06T7/0012 ,  G06T3/0075 ,  G06T7/44 ,  G06T2207/10081 ,  G06T2207/30061

    Abstract: The present invention is a multi-stage detection algorithm using a successive nodule candidate refinement approach. The detection algorithm involves four major steps. First, a lung region is segmented from a whole lung CT scan. This is followed by a hypothesis generation stage in which nodule candidate locations are identified from the lung region. In the third stage, nodule candidate sub-images or the lung region of the CT scan pass through a streaking artifact removal process. The nodule candidates are then successively refined using a sequence of filters of increasing complexity. A first filter uses attachment area information to remove vessels and large vessel bifurcation points from the nodule candidate list. A second filter removes small bifurcation points.

    Abstract translation: 本发明是使用连续结节候选细化方法的多级检测算法。 检测算法涉及四个主要步骤。 首先，从整个肺CT扫描分割肺部区域。 随后是从肺区识别结节候选位置的假设生成阶段。 在第三阶段，CT扫描的结节候选子图像或肺区域通过条纹伪影去除过程。 然后使用逐渐增加的复杂度的一系列滤波器连续地细化结节候选物。 第一个过滤器使用附件区域信息从结节候选列表中移除血管和大血管分叉点。 第二个过滤器移除小分叉点。

    System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans
    16.
    发明授权
    System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans 有权
    用于计算机断层扫描的小型肺结节计算机辅助诊断系统，方法和装置

    公开(公告)号：US07660451B2

    公开(公告)日：2010-02-09

    申请号：US12074211

    申请日：2008-02-29

    Applicant: Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henshke ,  Antoni Chan

    Inventor： Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henshke ,  Antoni Chan

    IPC: G06K9/62

    CPC classification number: A61B6/5258 ,  G06T5/20 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/155 ,  G06T2207/10081 ,  G06T2207/30064

    Abstract: The present invention is directed to diagnostic imaging of small pulmonary nodules. There are two main stages in the evaluation of pulmonary nodules from Computed Tomography (CT) scans: detection, in which the locations of possible nodules are identified, and characterization, in which a nodule is represented by measured features that may be used to evaluate the probability that the nodule is cancer. Currently, the most useful prediction feature is growth rate, which requires the comparison of size estimates from two CT scans recorded at different times. The present invention includes methods for detection and feature extraction for size characterization. The invention focuses the analysis of small pulmonary nodules that are less than 1 centimeter in size, but is also suitable for larger nodules as well.

    Abstract translation: 本发明涉及小型肺结节的诊断成像。 计算机断层扫描（CT）扫描对肺结节进行评估有两个主要阶段：其中确定可能结节位置的检测和表征，其中结节由可用于评估结节的测量特征表示 结节是癌症的概率。 目前，最有用的预测特征是增长率，这需要比较不同时间记录的两个CT扫描的大小估计。 本发明包括用于尺寸表征的检测和特征提取的方法。 本发明重点分析小小于1厘米的小肺结节，但也适用于较大的结节。

    System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans
    18.
    发明申请
    System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans 有权
    用于计算机断层扫描的小型肺结节计算机辅助诊断系统，方法和装置

    公开(公告)号：US20080187204A1

    公开(公告)日：2008-08-07

    申请号：US12074211

    申请日：2008-02-29

    Applicant: Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henshke ,  Antoni Chan

    Inventor： Anthony P. Reeves ,  David Yankelevitz ,  Claudia Henshke ,  Antoni Chan

    IPC: G06K9/00

    CPC classification number: A61B6/5258 ,  G06T5/20 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/155 ,  G06T2207/10081 ,  G06T2207/30064

    Abstract: The present invention is directed to diagnostic imaging of small pulmonary nodules. There are two main stages in the evaluation of pulmonary nodules from Computed Tomography (CT) scans: detection, in which the locations of possible nodules are identified, and characterization, in which a nodule is represented by measured features that may be used to evaluate the probability that the nodule is cancer. Currently, the most useful prediction feature is growth rate, which requires the comparison of size estimates from two CT scans recorded at different times. The present invention includes methods for detection and feature extraction for size characterization. The invention focuses the analysis of small pulmonary nodules that are less than 1 centimeter in size, but is also suitable for larger nodules as well.

    Abstract translation: 本发明涉及小型肺结节的诊断成像。 计算机断层扫描（CT）扫描的肺结节评估有两个主要阶段：其中确定可能结节的位置的检测和表征，其中结节由可用于评估结节的测量特征表示 结节是癌症的概率。 目前，最有用的预测特征是增长率，这需要比较不同时间记录的两个CT扫描的大小估计。 本发明包括用于尺寸表征的检测和特征提取的方法。 本发明重点分析小小于1厘米的小肺结节，但也适用于较大的结节。

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