Taunk et al., 2019 - Google Patents
Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopyTaunk et al., 2019
- Document ID
- 1974996168394066979
- Author
- Taunk P
- Atkinson C
- Lichtenstein D
- Rodriguez-Diaz E
- Singh S
- Publication year
- Publication venue
- International Journal of Colorectal Disease
External Links
Snippet
Introduction Probe-based confocal laser endomicroscopy (pCLE) is a promising modality for classifying polyp histology in vivo, but decision making in real-time is hampered by high- magnification targeting and by the learning curve for image interpretation. The aim of this …
- 239000000523 sample 0 title abstract description 29
Classifications
-
- 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
-
- 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
- G06T2207/30092—Stomach; Gastric
-
- 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
- G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
-
- 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
- G06T2207/20112—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
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
AU2019431299B2 (en) | AI systems for detecting and sizing lesions | |
Kamba et al. | Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial | |
Hassan et al. | Artificial intelligence allows leaving-in-situ colorectal polyps | |
Mori et al. | Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study | |
Gottlieb et al. | Central reading of ulcerative colitis clinical trial videos using neural networks | |
Kominami et al. | Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy | |
Zhao et al. | Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy | |
Sikka et al. | Comparison of white light and narrow band high definition images in predicting colon polyp histology, using standard colonoscopes without optical magnification | |
Miyaki et al. | A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer | |
Taunk et al. | Computer-assisted assessment of colonic polyp histopathology using probe-based confocal laser endomicroscopy | |
Ştefănescu et al. | Computer aided diagnosis for confocal laser endomicroscopy in advanced colorectal adenocarcinoma | |
Marion et al. | Chromoendoscopy-targeted biopsies are superior to standard colonoscopic surveillance for detecting dysplasia in inflammatory bowel disease patients: a prospective endoscopic trial | |
Miyaki et al. | Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement | |
Rodriguez-Diaz et al. | Real-time artificial intelligence–based histologic classification of colorectal polyps with augmented visualization | |
Jha et al. | Gastrovision: A multi-class endoscopy image dataset for computer aided gastrointestinal disease detection | |
Riegler et al. | Eir—efficient computer aided diagnosis framework for gastrointestinal endoscopies | |
Suzuki et al. | Artificial intelligence for cancer detection of the upper gastrointestinal tract | |
Wang et al. | A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation | |
Sakamoto et al. | Performance of computer-aided detection and diagnosis of colorectal polyps compares to that of experienced endoscopists | |
Choi et al. | Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms | |
Syed et al. | Artificial intelligence and its role in identifying esophageal neoplasia | |
Song et al. | Use of artificial intelligence to improve the quality control of gastrointestinal endoscopy | |
Klenske et al. | I-scan optical enhancement for the in vivo prediction of diminutive colorectal polyp histology: results from a prospective three-phased multicentre trial | |
Jin et al. | Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video) | |
Jiang et al. | Toward real-time quantification of fluorescence molecular probes using target/background ratio for guiding biopsy and endoscopic therapy of esophageal neoplasia |