Nothing Special   »   [go: up one dir, main page]

Giacomello et al., 2020 - Google Patents

Brain MRI tumor segmentation with adversarial networks

Giacomello et al., 2020

View PDF
Document ID
9859631315353674340
Author
Giacomello E
Loiacono D
Mainardi L
Publication year
Publication venue
2020 International Joint Conference on Neural Networks (IJCNN)

External Links

Snippet

Deep Learning is a promising approach to either automate or simplify several tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an end-to-end approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based on Adversarial Networks …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
    • G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • G06T2207/20076—Probabilistic image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • 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
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Xue et al. Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation
Tajbakhsh et al. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Haque et al. Deep learning approaches to biomedical image segmentation
Mazurowski et al. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Yousef et al. A holistic overview of deep learning approach in medical imaging
US11730387B2 (en) Method for detection and diagnosis of lung and pancreatic cancers from imaging scans
Yamashita et al. Convolutional neural networks: an overview and application in radiology
Kamnitsas et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
George et al. Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans
Giacomello et al. Brain MRI tumor segmentation with adversarial networks
Carneiro et al. Review of deep learning methods in mammography, cardiovascular, and microscopy image analysis
Tuan et al. Brain tumor segmentation using bit-plane and UNET
Indraswari et al. Multi-projection deep learning network for segmentation of 3D medical images
Zhang et al. A survey on deep learning of small sample in biomedical image analysis
Kaur et al. GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets
Agravat et al. Deep learning for automated brain tumor segmentation in mri images
US12106225B2 (en) System, method, and computer-accessible medium for generating multi-class models from single-class datasets
US20220051402A1 (en) Systems for automated lesion detection and related methods
Vesal et al. Automated multi-sequence cardiac MRI segmentation using supervised domain adaptation
Jyotiyana et al. Deep learning and the future of biomedical image analysis
Puch et al. Global planar convolutions for improved context aggregation in brain tumor segmentation
Sachdeva et al. The power of deep learning for intelligent tumor classification systems: A review
Ali et al. Multi-level Kronecker Convolutional Neural Network (ML-KCNN) for glioma segmentation from multi-modal MRI volumetric data
Lahoti et al. Whole Tumor Segmentation from Brain MR images using Multi-view 2D Convolutional Neural Network
Patel et al. Multi-class brain tumor segmentation using graph attention network