Dey et al., 2021 - Google Patents
Computed tomography and artificial intelligenceDey et al., 2021
- Document ID
- 16656020422278603250
- Author
- Dey D
- Lin A
- Han D
- Slomka P
- Publication year
- Publication venue
- Machine learning in cardiovascular medicine
External Links
Snippet
In the past 2 decades, significant advances in computed tomography (CT) hardware and software have expanded the clinical utility of CT imaging. Hardware improvements include faster gantry rotation with improved temporal resolution, improved spatial resolution, and …
- 238000002591 computed tomography 0 title abstract description 208
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
- G06T2207/10104—Positron emission tomography [PET]
-
- 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
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- 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
-
- 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
- G06T2207/20212—Image combination
- G06T2207/20224—Image subtraction
-
- 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
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20101—Interactive definition of point of interest, landmark or seed
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/50—Clinical applications
- A61B6/504—Clinical applications involving diagnosis of blood vessels, e.g. by angiography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/50—Clinical applications
- A61B6/507—Clinical applications involving determination of haemodynamic parameters, e.g. perfusion CT
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/48—Diagnostic techniques
- A61B6/481—Diagnostic techniques involving the use of contrast agents
-
- 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
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Litjens et al. | State-of-the-art deep learning in cardiovascular image analysis | |
Lessmann et al. | Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions | |
Aljabri et al. | A review on the use of deep learning for medical images segmentation | |
JP7149286B2 (en) | Method and system for assessing vascular occlusion based on machine learning | |
Yasaka et al. | Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study | |
Jacobs et al. | Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images | |
Shadmi et al. | Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT | |
Wan et al. | Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging | |
Cong et al. | Automated stenosis detection and classification in x-ray angiography using deep neural network | |
Jafari et al. | Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review | |
JP2018511443A (en) | Dual energy X-ray coronary calcium grading | |
Summers et al. | Atherosclerotic plaque burden on abdominal CT: automated assessment with deep learning on noncontrast and contrast-enhanced scans | |
Mannil et al. | Artificial intelligence and texture analysis in cardiac imaging | |
Chang et al. | Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium | |
Rueckel et al. | Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance | |
Salahuddin et al. | Multi-resolution 3d convolutional neural networks for automatic coronary centerline extraction in cardiac CT angiography scans | |
Cong et al. | Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography | |
Yang | Application of artificial intelligence to cardiovascular computed tomography | |
Shahzad et al. | A patient-specific coronary density estimate | |
Aromiwura et al. | Artificial intelligence in cardiac computed tomography | |
Ahmed et al. | A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques | |
Mäkelä et al. | Automatic CT angiography lesion segmentation compared to CT perfusion in ischemic stroke detection: a feasibility study | |
Ahmadi et al. | Comparative analysis of segment anything model and u-net for breast tumor detection in ultrasound and mammography images | |
Jawaid et al. | A hybrid energy model for region based curve evolution–Application to CTA coronary segmentation | |
Dey et al. | Computed tomography and artificial intelligence |