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    AUTOMATIC ORGAN LOCALIZATION
    1.
    发明申请
    AUTOMATIC ORGAN LOCALIZATION 有权
    自动机构本地化

    公开(公告)号：US20120269407A1

    公开(公告)日：2012-10-25

    申请号：US13090108

    申请日：2011-04-19

    Applicant: Antonio CRIMINISI ,  Jamie Daniel Joseph SHOTTON ,  Duncan Paul ROBERTSON ,  Sayan D. PATHAK ,  Steven James WHITE ,  Khan Mohammed SIDDIQUI

    Inventor： Antonio CRIMINISI ,  Jamie Daniel Joseph SHOTTON ,  Duncan Paul ROBERTSON ,  Sayan D. PATHAK ,  Steven James WHITE ,  Khan Mohammed SIDDIQUI

    IPC: G06K9/00

    CPC classification number: G06T7/0048 ,  G06K2209/051 ,  G06T7/77 ,  G06T2207/10072 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/30004

    Abstract: Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.

    Abstract translation: 描述自动器官定位。 在一个示例中，使用一个或多个经过训练的回归树将医学图像中的器官定位。 将医学图像的每个图像元素应用于经过训练的回归树，以计算与从每个图像元素到器官的距离相关的概率分布。 选择和聚合概率分布的至少一个子集以计算器官的定位估计。 在另一示例中，使用具有预定义的器官位置的训练图像来训练回归树。 在树的每个节点，生成测试参数，确定每个训练图像元素传递到哪个后续节点。 这是重复的，直到每个图像元素到达树的叶节点。 基于从叶节点的图像元素到器官的距离，在每个叶节点处生成并存储概率分布。

    SECURE PATIENT INFORMATION HANDLING
    2.
    发明申请
    SECURE PATIENT INFORMATION HANDLING 审中-公开
    安全患者信息处理

    公开(公告)号：US20130006867A1

    公开(公告)日：2013-01-03

    申请号：US13172885

    申请日：2011-06-30

    Applicant: Bryan Dove ,  Sean Nolan ,  John C. Gillotte ,  Khan M. Siddiqui ,  Steven J. White ,  Sayan D. Pathak ,  Siddhartha K. Singh

    Inventor： Bryan Dove ,  Sean Nolan ,  John C. Gillotte ,  Khan M. Siddiqui ,  Steven J. White ,  Sayan D. Pathak ,  Siddhartha K. Singh

    IPC: G06Q50/00 ,  G06F17/30 ,  H04L9/28

    CPC classification number: H04L9/083 ,  H04L2209/88

    Abstract: The description relates to secure patient information handling. One example can receive encrypted patient data from a first entity. The example can receive a request to view the encrypted patient data from a second entity. The request can include a conditional access code and the example can validate the conditional access code. In an instance where the conditional access code is valid, the example can retrieve an encryption key for the encrypted patient data. The example can decrypt at least a portion of the encrypted patient data to produce decrypted patient data. The example can provide at least some of the decrypted patient data to the second entity.

    Abstract translation: 该描述涉及安全的患者信息处理。 一个示例可以从第一实体接收加密的患者数据。 该示例可以接收从第二实体查看加密的患者数据的请求。 请求可以包括条件访问代码，该示例可以验证条件访问代码。 在条件访问代码有效的情况下，该示例可以检索加密的患者数据的加密密钥。 该示例可以解密加密的患者数据的至少一部分以产生解密的患者数据。 该示例可以将解密的患者数据中的至少一些提供给第二实体。

    Automatic organ localization
    4.
    发明授权
    Automatic organ localization 有权
    自动器官定位

    公开(公告)号：US08867802B2

    公开(公告)日：2014-10-21

    申请号：US13090108

    申请日：2011-04-19

    Applicant: Antonio Criminisi ,  Jamie Daniel Joseph Shotton ,  Duncan Paul Robertson ,  Sayan D. Pathak ,  Steven James White ,  Khan Mohammed Siddiqui

    Inventor： Antonio Criminisi ,  Jamie Daniel Joseph Shotton ,  Duncan Paul Robertson ,  Sayan D. Pathak ,  Steven James White ,  Khan Mohammed Siddiqui

    IPC: G06K9/00 ,  G06T7/00

    CPC classification number: G06T7/0048 ,  G06K2209/051 ,  G06T7/77 ,  G06T2207/10072 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/30004

    Abstract: Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.

    Abstract translation: 描述自动器官定位。 在一个示例中，使用一个或多个经过训练的回归树将医学图像中的器官定位。 将医学图像的每个图像元素应用于经过训练的回归树，以计算与从每个图像元素到器官的距离相关的概率分布。 选择和聚合概率分布的至少一个子集以计算器官的定位估计。 在另一示例中，使用具有预定义的器官位置的训练图像来训练回归树。 在树的每个节点，生成测试参数，确定每个训练图像元素传递到哪个后续节点。 这是重复的，直到每个图像元素到达树的叶节点。 基于从叶节点的图像元素到器官的距离，在每个叶节点处生成并存储概率分布。

    Pubic arch detection and interference assessment in transrectal ultrasound guided prostate cancer therapy
    7.
    发明授权
    Pubic arch detection and interference assessment in transrectal ultrasound guided prostate cancer therapy 失效
    经直肠超声引导前列腺癌治疗阴道弓检测和干扰评估

    公开(公告)号：US6027446A

    公开(公告)日：2000-02-22

    申请号：US187830

    申请日：1998-11-06

    Applicant: Sayan D. Pathak ,  Peter D. Grimm ,  Yongmin Kim

    Inventor： Sayan D. Pathak ,  Peter D. Grimm ,  Yongmin Kim

    IPC: A61N5/10 ,  G01S7/52 ,  A61B8/00

    CPC classification number: G01S7/52036 ,  A61N2005/1012 ,  A61N5/1027

    Abstract: Transrectal ultrasound is used for accurate planning and targeting of placement of radioactive seeds by needles into the prostate in transperineal prostate brachytherapy. The pubic arch formed by the union of the pelvic bones is a potential barrier to passage of the needles containing the radioactive seeds. An initial image of the pubic arch is also provided by transrectal ultrasound, in addition to a transverse cross-sectional image of the prostate. The initial image of the pubic arch is processed using a technique to selectively enhance the contrast of the linear features of the edge of the ultrasound image. The enhanced image is then thresholded via the use of a percentile thresholding technique and then a curve modeling the shape of the pubic arch is fitted against the thresholded, enhanced image to produce a processed image of the pubic arch. This is then overlaid with the image of the prostate and a determination made as to pubic arch interference.

    Abstract translation: 经直肠超声用于精确规划和针对放射性种子放置在经前列腺近距离放射治疗中的前列腺。 由骨盆骨联合形成的耻骨弓是含有放射性种子的针通过的潜在障碍。 除了前列腺的横截面图像之外，经直肠超声还提供耻骨弓的初始图像。 使用用于选择性地增强超声图像的边缘的线性特征的对比度的技术来处理耻骨弓的初始图像。 然后通过使用百分位数阈值技术对增强图像进行阈值化，然后将模拟耻骨弓形状的曲线拟合到阈值增强图像上，以产生耻骨弓的经处理图像。 然后将其与前列腺的图像重叠，并确定对耻骨弓干扰。

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