公开(公告)号：US20220406049A1

公开(公告)日：2022-12-22

申请号：US17845880

申请日：2022-06-21

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Dibson Gondim ,  Ahmed Naglah ,  Fahmi Khalifa

IPC分类号： G06V10/82 ,  G06V10/778 ,  G06T7/38 ,  G06V10/26 ,  G16H50/20 ,  G16H30/20 ,  G06N20/00

摘要： A novel system and method for accurate detection and quantification of fibrous tissue produces a virtual medical image of tissue treated with a second stain based on a received medical image of tissue treated with a first stain using a computer-implemented trained deep learning model. The model is trained to learn the deep texture patterns associated with collagen fibers using conditional generative adversarial networks to detect and quantify fibrous tissue.

公开(公告)号：US11495327B2

公开(公告)日：2022-11-08

申请号：US16030296

申请日：2018-07-09

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Ahmed Shalaby ,  Fahmi Khalifa ,  Islam Abdelmaksoud

IPC分类号： G16B40/00 ,  G16B5/00

摘要： Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.

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：US20130294669A1

公开(公告)日：2013-11-07

申请号：US13834304

申请日：2013-03-15

申请人： UNIVERSITY OF LOUISVILLE RESEARCH FOUNDATION, INC.

发明人： Ayman S. El-Baz ,  Matthew Nitzken ,  Garth M. Beache

IPC分类号： G06T7/00

CPC分类号： G06T7/00 ,  G06K9/469 ,  G06K9/6297 ,  G06K2209/051 ,  G06T5/002 ,  G06T7/0012 ,  G06T7/42 ,  G06T7/46 ,  G06T2207/10088 ,  G06T2207/20056 ,  G06T2207/20076 ,  G06T2207/30048

摘要： A computer aided image processing system and automated method to improve tagged magnetic resonance image data through modeling and analyzing the magnetic resonance image data using a linear combination of discrete Gaussians model and using a Markov-Gibbs random field model. The processed magnetic resonance images include reduced noise associated with the tags, augmented gradients across a tag profile and an amplified tag to background contrast as compared to the original tagged magnetic resonance images.

摘要翻译： 一种计算机辅助图像处理系统和自动化方法，通过使用离散高斯模型的线性组合和使用马尔可夫吉布斯随机场模型对磁共振图像数据进行建模和分析来改进标记的磁共振图像数据。 处理的磁共振图像包括与标签相关联的减少的噪声，与标记的磁共振图像相比，横跨标签轮廓的增强梯度和放大的标签与背景对比。

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：US20200285714A9

公开(公告)日：2020-09-10

申请号：US16030296

申请日：2018-07-09

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Ahmed Shalaby ,  Fahmi Khalifa ,  Islam Abdelmaksoud

IPC分类号： G06F19/24 ,  G06F19/12

摘要： Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.

公开(公告)号：US20200012761A1

公开(公告)日：2020-01-09

申请号：US16030296

申请日：2018-07-09

申请人： University of Louisville Research Foundation, Inc.

发明人： Ayman S. El-Baz ,  Ahmed Shalaby ,  Fahmi Khalifa ,  Islam Abdelmaksoud

IPC分类号： G06F19/24 ,  G06F19/12

摘要： Systems and methods for diagnosing prostate cancer. Image sets (e.g., MRI collected at one or more b-values) and biological values (e.g., prostate specific antigen (PSA)) have features extracted and integrated to produce a diagnosis of prostate cancer. The image sets are analyzed primarily in three steps: (1) segmentation, (2) feature extraction, smoothing, and normalization, and (3) classification. The biological values are analyzed primarily in two steps: (1) feature extraction and (2) classification. Each analysis results in diagnostic probabilities, which are then combined to pass through an additional classification stage. The end result is a more accurate diagnosis of prostate cancer.

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.