Wei et al., 2014 - Google Patents
Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysisWei et al., 2014
View PDF- Document ID
- 11289132561967471287
- Author
- Wei J
- Zhou C
- Chan H
- Chughtai A
- Agarwal P
- Kuriakose J
- Hadjiiski L
- Patel S
- Kazerooni E
- Publication year
- Publication venue
- Medical physics
External Links
Snippet
Purpose: The buildup of noncalcified plaques (NCPs) that are vulnerable to rupture in coronary arteries is a risk for myocardial infarction. Interpretation of coronary CT angiography (cCTA) to search for NCP is a challenging task for radiologists due to the low …
- 238000001514 detection method 0 title abstract description 47
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30048—Heart; Cardiac
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20156—Automatic seed setting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
- G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11302002B2 (en) | Systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking | |
| Mannil et al. | Texture analysis and machine learning for detecting myocardial infarction in noncontrast low-dose computed tomography: unveiling the invisible | |
| Karim et al. | Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images | |
| van Assen et al. | Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT: a validation study | |
| Wei et al. | Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysis | |
| Brown et al. | Lung micronodules: automated method for detection at thin-section CT—initial experience | |
| Shahzad et al. | Vessel specific coronary artery calcium scoring: an automatic system | |
| Doi et al. | Digital radiography: A useful clinical tool for computer-aided diagnosis by quantitative analysis of radiographic images | |
| Reeves et al. | Computer-aided diagnosis for lung cancer | |
| Summers et al. | Atherosclerotic plaque burden on abdominal CT: automated assessment with deep learning on noncontrast and contrast-enhanced scans | |
| Blackmon et al. | Computer-aided detection of pulmonary embolism at CT pulmonary angiography: can it improve performance of inexperienced readers? | |
| Guo et al. | Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography | |
| Xu et al. | Quantifying the margin sharpness of lesions on radiological images for content‐based image retrieval | |
| García et al. | Evaluation of texture for classification of abdominal aortic aneurysm after endovascular repair | |
| Jin et al. | Automatic coronary plaque detection, classification, and stenosis grading using deep learning and radiomics on computed tomography angiography images: a multi-center multi-vendor study | |
| Hong et al. | Automated coronary artery calcium scoring using nested U-Net and focal loss | |
| Kang et al. | Automated knowledge‐based detection of nonobstructive and obstructive arterial lesions from coronary CT angiography | |
| Zhang et al. | Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study | |
| Bento et al. | Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set | |
| Išgum et al. | Automated aortic calcium scoring on low‐dose chest computed tomography | |
| Bhushan | Liver cancer detection using hybrid approach-based convolutional neural network (HABCNN) | |
| Zhou et al. | Computerized analysis of coronary artery disease: performance evaluation of segmentation and tracking of coronary arteries in CT angiograms | |
| Zhou et al. | Computer‐aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): Performance evaluation with independent data sets | |
| Zhou et al. | Coronary artery analysis: Computer‐assisted selection of best‐quality segments in multiple‐phase coronary CT angiography | |
| Alvén et al. | PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography |