公开(公告)号：US11534064B2

公开(公告)日：2022-12-27

申请号：US16620496

申请日：2018-06-20

申请人： University of Louisville Research Foundation, Inc. ,  University of Massachusetts

发明人： Ayman El-Baz ,  Nabila Eldawi ,  Shlomit Schaal ,  Mohammed Elmogy ,  Harpal Sandhu ,  Robert S. Keynton ,  Ahmed Soliman

IPC分类号： A61B3/12 ,  G06T7/143 ,  G06T7/11 ,  G06T5/00 ,  G06T7/00 ,  G06V10/40

摘要： Methods for automated segmentation system for retinal blood vessels from optical coherence tomography angiography images include a preprocessing stage, an initial segmentation stage, and a refining stage. Application of machine-learning techniques to segmented images allow for automated diagnosis of retinovascular diseases, such as diabetic retinopathy.

公开(公告)号：US11481905B2

公开(公告)日：2022-10-25

申请号：US17050269

申请日：2019-04-26

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Ahmed Soliman ,  Ahmed Eltanboly ,  Ahmed Sleman ,  Robert S. Keynton ,  Harpal Sandhu ,  Andrew Switala

IPC分类号： G06K9/00 ,  G06T7/143

摘要： A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

公开(公告)号：US11238975B2

公开(公告)日：2022-02-01

申请号：US16282753

申请日：2019-02-22

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Amy Dwyer ,  Ahmed Soliman ,  Mohamed Shehata ,  Hisham Abdeltawab ,  Fahmi Khalifa

IPC分类号： G16H30/40 ,  G06T7/143 ,  G06T7/12 ,  G06T7/33 ,  G06T7/00 ,  G16H30/20 ,  G16H50/20 ,  A61B5/20 ,  A61B5/026 ,  A61B5/00 ,  A61B5/0295 ,  G16H50/30 ,  A61B5/145

摘要： A computer aided diagnostic system and automated method to classify a kidney utilizes medical image data and clinical biomarkers in evaluation of kidney function pre- and post-transplantation. The system receives image data from a medical scan that includes image data of a kidney, then segments kidney image data from other image data of the medical scan. The kidney is then classified by analyzing at least one feature determined from the kidney image data and the at least one clinical biomarker.

公开(公告)号：US10453569B2

公开(公告)日：2019-10-22

申请号：US15903769

申请日：2018-02-23

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Amy Dwyer ,  Rosemary Ouseph ,  Fahmi Khalifa ,  Ahmed Soliman ,  Mohamed Shehata

IPC分类号： G16H30/40 ,  A61B5/20 ,  G06F19/00 ,  G06T7/00 ,  G06T7/33 ,  G06T7/12 ,  G06T7/143 ,  G16H50/20 ,  G16H30/20

摘要： A computer aided diagnostic system and automated method to classify a kidney. Image data for a medical scan that includes image data of a kidney may be received. The kidney image data may be segmented from other image data of the medical scan. One or more iso-contours may be registered for the kidney image data, and renal cortex image data may be segmented from the kidney image data based on the one or more registered iso-contours. The kidney may be classified by analyzing one or more features determined from the segmented renal cortex image data using a learned model associated with the one or more features.

公开(公告)号：US20190237186A1

公开(公告)日：2019-08-01

申请号：US16282753

申请日：2019-02-22

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Amy Dwyer ,  Ahmed Soliman ,  Mohamed Shehata ,  Hisham Abdeltawab ,  Fahmi Khalifa

IPC分类号： G16H30/40 ,  G06T7/143 ,  G06T7/12 ,  G06T7/33 ,  G06T7/00 ,  A61B5/20 ,  G16H30/20 ,  G16H50/20

CPC分类号： G16H30/40 ,  A61B5/201 ,  G06T7/0012 ,  G06T7/12 ,  G06T7/143 ,  G06T7/33 ,  G06T2207/10088 ,  G06T2207/10096 ,  G06T2207/20081 ,  G06T2207/30084 ,  G16H30/20 ,  G16H50/20

摘要： A computer aided diagnostic system and automated method to classify a kidney utilizes medical image data and clinical biomarkers in evaluation of kidney function pre- and post-transplantation. The system receives image data from a medical scan that includes image data of a kidney, then segments kidney image data from other image data of the medical scan. The kidney is then classified by analyzing at least one feature determined from the kidney image data and the at least one clinical biomarker.

公开(公告)号：US20210082123A1

公开(公告)日：2021-03-18

申请号：US17050269

申请日：2019-04-26

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Ahmed Soliman ,  Ahmed Eltanboly ,  Ahmed Sleman ,  Robert S. Keynton ,  Harpal Sandhu ,  Andrew Switala

IPC分类号： G06T7/143

摘要： A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.

公开(公告)号：US20180070905A1

公开(公告)日：2018-03-15

申请号：US15704719

申请日：2017-09-14

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Ahmed Soliman ,  Fahmi Khalifa ,  Ahmed Shaffie ,  Neal Dunlap ,  Brian Wang

IPC分类号： A61B6/00 ,  A61B6/03 ,  A61N5/10 ,  G06T7/33 ,  G06T7/149 ,  G06T7/143 ,  G06T7/174 ,  G06T7/38 ,  G06T7/246

摘要： A system and computation method is disclosed that identifies radiation-induced lung injury after radiation therapy using 4D computed tomography (CT) scans. After deformable image registration, the method segments lung fields, extracts functional and textural features, and classifies lung tissues. The deformable registration locally aligns consecutive phases of the respiratory cycle using gradient descent minimization of the conventional dissimilarity metric. Then an adaptive shape prior, a first-order intensity model, and a second-order lung tissues homogeneity descriptor are integrated to segment the lung fields. In addition to common lung functionality features, such as ventilation and elasticity, specific regional textural features are estimated by modeling the segmented images as samples of a novel 7th-order contrast-offset-invariant Markov-Gibbs random field (MGRF). Finally, a tissue classifier is applied to distinguish between the injured and normal lung tissues.

公开(公告)号：US20150286786A1

公开(公告)日：2015-10-08

申请号：US14676111

申请日：2015-04-01

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Amy Dwyer ,  Rosemary Ouseph ,  Fahmi Khalifa ,  Ahmed Soliman ,  Mohamed Shehata

IPC分类号： G06F19/00 ,  G06K9/46 ,  G06T7/00 ,  G06K9/62 ,  A61B5/20 ,  G06K9/34

CPC分类号： G16H30/40 ,  A61B5/201 ,  G06F19/00 ,  G06F19/321 ,  G06T7/0012 ,  G06T7/12 ,  G06T7/143 ,  G06T7/33 ,  G06T2207/10088 ,  G06T2207/10096 ,  G06T2207/20081 ,  G06T2207/30084 ,  G16H30/20 ,  G16H50/20

摘要： A computer aided diagnostic system and automated method to classify a kidney. Image data for a medical scan that includes image data of a kidney may be received. The kidney image data may be segmented from other image data of the medical scan. One or more iso-contours may be registered for the kidney image data, and renal cortex image data may be segmented from the kidney image data based on the one or more registered iso-contours. The kidney may be classified by analyzing one or more features determined from the segmented renal cortex image data using a learned model associated with the one or more features.

摘要翻译： 计算机辅助诊断系统和自动分类肾脏的方法。 可以接收包括肾脏的图像数据的医学扫描的图像数据。 肾图像数据可以从医学扫描的其他图像数据分割。 可以为肾图像数据注册一个或多个等值线，并且可以基于一个或多个注册的等轮廓从肾图像数据中分割肾皮质图像数据。 可以使用与一个或多个特征相关联的学习模型分析从分割的肾皮质图像数据确定的一个或多个特征来分类肾脏。

公开(公告)号：US11875892B2

公开(公告)日：2024-01-16

申请号：US16628795

申请日：2018-07-07

申请人： UNIVERSITY OF LOUISVILLE RESEARCH FOUNDATION, INC. ,  SCIENCE AND TECHNOLOGY DEVELOPMENT FUND

发明人： Ayman S. El-Baz ,  Ahmed Soliman ,  Moumen El-Melegy ,  Mohamed Abou El-Ghar

IPC分类号： G16H30/40 ,  G06T7/11 ,  G06N20/20 ,  G06N3/08 ,  G06N7/01

CPC分类号： G16H30/40 ,  G06N3/08 ,  G06N7/01 ,  G06N20/20 ,  G06T7/11 ,  G06T2207/20081 ,  G06T2207/20084

摘要： Methods for segmenting medical images from different modalities include integrating a plurality of types of quantitative image descriptors with a deep 3D convolutional neural network. The descriptors include: (i) a Gibbs energy for a prelearned 7th-order Markov-Gibbs random field (MGRF) model of visual appearance, (ii) an adaptive shape prior model, and (iii) a first-order appearance model of the original volume to be segmented. The neural network fuses the computed descriptors to obtain the final voxel-wise probabilities of the goal regions.

公开(公告)号：US20230230705A1

公开(公告)日：2023-07-20

申请号：US18182221

申请日：2023-03-10

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Mohamed Elsharkawy ,  Ahmed Sharafeldeen ,  Ahmed Shalaby ,  Ahmed Soliman ,  Ali Mahmoud ,  Harpal Sandhu ,  Guruprasad A. Giridharan

IPC分类号： G16H50/20 ,  G06T7/00 ,  G06T7/11

CPC分类号： G16H50/20 ,  G06T7/0012 ,  G06T7/11 ,  G06T2207/30061

摘要： Assessment of pulmonary function in coronavirus patients includes use of a computer aided diagnostic system to assess pulmonary function and risk of mortality in patents with coronavirus disease 2019. The CAD system processes thoracic X-ray data from a patient, extracts imaging markers, and grades disease severity based at least in part on the extracted imaging markers, thereby distinguishing between higher risk and lower risk patients. An alternative approach is to use an automatic CAD system to grade COVID-19 from computed tomography (CT) images to determine an accurate diagnosis of lung function.