[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

He et al., 2017 - Google Patents

Towards topological correct segmentation of macular OCT from cascaded FCNs

He et al., 2017

View HTML
Document ID
2075120428957893705
Author
He Y
Carass A
Yun Y
Zhao C
Jedynak B
Solomon S
Saidha S
Calabresi P
Prince J
Publication year
Publication venue
Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 4

External Links

Snippet

Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. In particular, OCT is used to study the changes in layer thickness across various pathologies. The …
Continue reading at www.ncbi.nlm.nih.gov (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30172Centreline of tubular or elongated structure
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation

Similar Documents

Publication Publication Date Title
He et al. Towards topological correct segmentation of macular OCT from cascaded FCNs
Lu et al. Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network
Zhao et al. Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images
Arsalan et al. Aiding the diagnosis of diabetic and hypertensive retinopathy using artificial intelligence-based semantic segmentation
Apostolopoulos et al. Pathological OCT retinal layer segmentation using branch residual U-shape networks
Paul Cohen et al. Count-ception: Counting by fully convolutional redundant counting
Halupka et al. Retinal optical coherence tomography image enhancement via deep learning
He et al. Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT
Sedai et al. Joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using Bayesian deep learning
Estienne et al. U-ReSNet: Ultimate coupling of registration and segmentation with deep nets
Tennakoon et al. Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks
He et al. Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks
Hu et al. Automatic segmentation of retinal layer boundaries in OCT images using multiscale convolutional neural network and graph search
Chen et al. Automated segmentation of the choroid in EDI-OCT images with retinal pathology using convolution neural networks
Novosel et al. Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography
Sarhan et al. Multi-scale microaneurysms segmentation using embedding triplet loss
Shah et al. Simultaneous multiple surface segmentation using deep learning
Eladawi et al. Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images
Sousa et al. Automatic segmentation of retinal layers in OCT images with intermediate age-related macular degeneration using U-Net and DexiNed
Asgari et al. U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography
CN109671049B (en) Medical image processing method, system, equipment and storage medium
Asgari et al. Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography
He et al. Choroid Segmentation of Retinal OCT Images Based on CNN Classifier and l2‐lq Fitter
Lin et al. Recent advanced deep learning architectures for retinal fluid segmentation on optical coherence tomography images
Rabbani et al. Obtaining thickness maps of corneal layers using the optimal algorithm for intracorneal layer segmentation