Guo, 2019 - Google Patents
Deep learning meets graph: novel hybrid methods for improved medical image analysisGuo, 2019
- Document ID
- 17668693544391900218
- Author
- Guo Z
- Publication year
External Links
Snippet
Medical image analysis plays a vital role in medical diagnostics, helping physicians monitor disease progression, select the most proper treatment plans in clinical practice, and in many other cases. In various image analysis tasks, image segmentation often serves as the first …
- 238000010191 image analysis 0 title abstract description 19
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
- 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
- G06T2207/10088—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
- G06T2207/10072—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
- G06T2207/10101—Optical tomography; Optical coherence tomography [OCT]
-
- 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
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30172—Centreline of tubular or elongated structure
-
- 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
- G06T2207/10024—Color 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
- G06T2207/20092—Interactive image processing based on input by user
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
- A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7542578B2 (en) | Methods and systems for utilizing quantitative imaging - Patents.com | |
| Bernard et al. | Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? | |
| Xia et al. | Recent advances of transformers in medical image analysis: a comprehensive review | |
| Van Rikxoort et al. | Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review | |
| Rudyanto et al. | Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study | |
| Carvalho et al. | 3D segmentation algorithms for computerized tomographic imaging: a systematic literature review | |
| Peng et al. | 3D liver segmentation using multiple region appearances and graph cuts | |
| Gao et al. | Automatic segmentation of coronary tree in CT angiography images | |
| Liao et al. | Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching | |
| Yao et al. | Advances on pancreas segmentation: a review | |
| Sfakianakis et al. | GUDU: Geometrically-constrained Ultrasound Data augmentation in U-Net for echocardiography semantic segmentation | |
| Campadelli et al. | A segmentation framework for abdominal organs from CT scans | |
| Ammari et al. | A review of approaches investigated for right ventricular segmentation using short‐axis cardiac MRI | |
| Sakellarios et al. | Novel methodology for 3D reconstruction of carotid arteries and plaque characterization based upon magnetic resonance imaging carotid angiography data | |
| Dharmalingham et al. | A model based segmentation approach for lung segmentation from chest computer tomography images | |
| Bai et al. | Automatic whole heart segmentation based on watershed and active contour model in CT images | |
| Liu et al. | Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment | |
| Guo | Deep learning meets graph: novel hybrid methods for improved medical image analysis | |
| Guo | Deep learning meets graph: novel hybrid methods for improved quantitative medical image analysis | |
| Serrano-Antón et al. | Unsupervised clustering based coronary artery segmentation | |
| Affane | Robust liver vessel segmentation in medical images using 3-D deep learning approaches | |
| Zhang | Efficient deep learning based assisted annotation for medical image segmentation | |
| Lyngdoh et al. | Liver Couinaud Segmentation: A Review on Existing Segmentation Techniques of the Couinaud Classification of Liver | |
| Sleman et al. | An innovative 3d adaptive patient-related atlas for automatic segmentation of retina layers from oct images | |
| 鈴木裕紀 | Segmentation of Blood Vessels and Pathological Regions from Computed Tomography Images using Convolutional Neural Networks |