[go: up one dir, main page]

Wu et al., 2017 - Google Patents

Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation

Wu et al., 2017

View PDF
Document ID
6739786036871118570
Author
Wu L
Xin Y
Li S
Wang T
Heng P
Ni D
Publication year
Publication venue
2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017)

External Links

Snippet

Computerized prenatal ultrasound (US) image segmentation methods can greatly improve the efficiency and objectiveness of the biometry interpretation. However, the boundary incompleteness and ambiguity in US images hinder the automatic solutions severely. In this …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • 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
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • 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
    • G06T2207/10Image 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications

Similar Documents

Publication Publication Date Title
Wu et al. Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation
US10482603B1 (en) Medical image segmentation using an integrated edge guidance module and object segmentation network
Adegun et al. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art
Li et al. Hierarchical deep network with uncertainty-aware semi-supervised learning for vessel segmentation
Das et al. Attention-UNet architectures with pretrained backbones for multi-class cardiac MR image segmentation
US9792694B2 (en) Segmentation using hybrid discriminative generative label fusion of multiple atlases
Klibisz et al. Fast, simple calcium imaging segmentation with fully convolutional networks
CN110189308B (en) Tumor detection method and device based on fusion of BM3D and dense convolution network
CN110991254B (en) Ultrasonic image video classification prediction method and system
US11120297B2 (en) Segmentation of target areas in images
Sirjani et al. Automatic cardiac evaluations using a deep video object segmentation network
Xie et al. Optic disc and cup image segmentation utilizing contour-based transformation and sequence labeling networks
CN110110727A (en) The image partition method post-processed based on condition random field and Bayes
Huang et al. Automatic Retinal Vessel Segmentation Based on an Improved U‐Net Approach
Khan et al. A framework for segmentation and classification of blood cells using generative adversarial networks
Umer et al. Breast cancer classification and segmentation framework using multiscale CNN and U‐shaped dual decoded attention network
Yasmin et al. Impact of fuzziness for skin lesion classification with transformer-based model
Chatterjee et al. A survey on techniques used in medical imaging processing
Naas et al. An explainable AI for breast cancer classification using vision Transformer (ViT)
US12333773B2 (en) Explaining a model output of a trained model
Tawfeeq et al. Predication of Most Significant Features in Medical Image by Utilized CNN and Heatmap.
Kolarik et al. Planar 3D transfer learning for end to end unimodal MRI unbalanced data segmentation
CN112086174B (en) A three-dimensional knowledge diagnosis model construction method and system
WO2024098379A1 (en) Fully automatic cardiac magnetic resonance imaging segmentation method based on dilated residual network
CN116205844A (en) Full-automatic heart magnetic resonance imaging segmentation method based on expansion residual error network