公开(公告)号：US20150279034A1

公开(公告)日：2015-10-01

申请号：US14665652

申请日：2015-03-23

Applicant: Riverain Technologies LLC

Inventor： Jason F. KNAPP ,  Praveen KAKUMANU ,  Steve W. WORRELL

IPC: G06T7/00 ,  G06T11/00 ,  A61B6/03 ,  G06T5/30 ,  G06T3/00 ,  G06T1/00 ,  G06T5/00

CPC classification number: G06T11/008 ,  A61B6/032 ,  A61B6/5217 ,  A61B6/5258 ,  G06T5/005 ,  G06T7/0012 ,  G06T7/136 ,  G06T2207/10081 ,  G06T2207/20016 ,  G06T2207/20036 ,  G06T2207/20076 ,  G06T2207/30064 ,  G06T2207/30068 ,  G06T2207/30101

Abstract: Image processing techniques may include a methodology for normalizing medical image and/or voxel data captured under different acquisition protocols and a methodology for suppressing selected anatomical structures from medical image and/or voxel data, which may result in improved detection and/or improved rendering of other anatomical structures. The technology presented here may be used, e.g., for improved nodule detection within computed tomography (CT) scans. While presented here in the context of nodules within the lungs, these techniques may be applicable in other contexts with little modification, for example, the detection of masses and/or microcalcifications in full field mammography or breast tomosynthesis based on the suppression of glandular structures, parenchymal and vascular structures in the breast.

Abstract translation: 图像处理技术可以包括用于归一化在不同采集协议下捕获的医学图像和/或体素数据的方法，以及用于从医学图像和/或体素数据抑制所选择的解剖结构的方法，其可以导致改进的检测和/或改进的 其他解剖结构。 这里提出的技术可以用于例如计算机断层摄影（CT）扫描中的改进的结节检测。 虽然这里在肺内的结节的上下文中呈现，但是这些技术可以适用于其他上下文，其中几乎没有修改，例如，基于抑制腺体结构的全场乳房X线照相术或乳房断层合成中的质量和/或微钙化的检测， 乳腺中的实质和血管结构。

公开(公告)号：US20230162493A1

公开(公告)日：2023-05-25

申请号：US17992307

申请日：2022-11-22

Applicant: Riverain Technologies LLC

Inventor： Steve W. WORRELL ,  Xiaotian WU ,  Garrett REGAN ,  Jason F. KNAPP ,  Ofer PINHASI ,  Terry DOLWICK ,  Jason MONNIN

IPC: G06V10/82 ,  G06T7/00

CPC classification number: G06V10/82 ,  G06T7/0012 ,  G06T2207/30048 ,  G06V2201/031

Abstract: Automated techniques may be used to process three-dimensional image sets obtained via computer tomography (CT), magnetic resonance imaging (MRI), and other techniques. Image data representing an anatomical feature (e.g., an aorta) may be automatically segmented to obtain mask data for the anatomical feature. The mask data may undergo automated centerline regression to obtain centerline data for the anatomical feature. Automated curved planar reformatting, using the centerline data, may be applied to the original image data and/or the mask data. The curved planar reformation results may be subjected to automated segmentation. The resulting image data set may be used for such purposes as visualization, disease detection, measurement of feature, and/or tracking the anatomical feature over time.