Zhao et al., 2021 - Google Patents
Deep learning of brain magnetic resonance images: A brief reviewZhao et al., 2021
- Document ID
- 9536457978509606084
- Author
- Zhao X
- Zhao X
- Publication year
- Publication venue
- Methods
External Links
Snippet
Magnetic resonance imaging (MRI) is one of the most popular techniques in brain science and is important for understanding brain function and neuropsychiatric disorders. However, the processing and analysis of MRI is not a trivial task with lots of challenges. Recently, deep …
- 210000004556 Brain 0 title abstract description 149
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
- G06T2207/10—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
- G06T2207/10—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
- G06T2207/10088—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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- 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
- G06T2207/20048—Transform domain processing
- G06T2207/20064—Wavelet transform [DWT]
-
- 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
- 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
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- 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
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
-
- 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 |
|---|---|---|
| Zhao et al. | Deep learning of brain magnetic resonance images: A brief review | |
| Jyothi et al. | Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review | |
| US11449759B2 (en) | Medical imaging diffeomorphic registration based on machine learning | |
| Yang et al. | MRI cross-modality image-to-image translation | |
| US12354256B2 (en) | Brain feature prediction using geometric deep learning on graph representations of medical image data | |
| US11158069B2 (en) | Unsupervised deformable registration for multi-modal images | |
| Gu et al. | MedSRGAN: medical images super-resolution using generative adversarial networks | |
| Peng et al. | The multimodal MRI brain tumor segmentation based on AD-Net | |
| Sun et al. | Anatomical attention guided deep networks for ROI segmentation of brain MR images | |
| Cheng et al. | CNNs based multi-modality classification for AD diagnosis | |
| Chartsias et al. | Multimodal MR synthesis via modality-invariant latent representation | |
| WO2022152866A1 (en) | Devices and process for synthesizing images from a source nature to a target nature | |
| Benou et al. | De-noising of contrast-enhanced MRI sequences by an ensemble of expert deep neural networks | |
| Zhang et al. | CT image classification based on convolutional neural network | |
| Ibrahim et al. | Brain image fusion using the parameter adaptive-pulse coupled neural network (PA-PCNN) and non-subsampled contourlet transform (NSCT) | |
| Ji et al. | Deep learning-based magnetic resonance image super-resolution: a survey | |
| Sinha et al. | TrIND: Representing Anatomical Tr ees by Denoising D iffusion of I mplicit N eural Fields | |
| Wang et al. | Two-stage CNN whole heart segmentation combining image enhanced attention mechanism and metric classification | |
| Hua et al. | Multi-scale knowledge transfer vision transformer for 3D vessel shape segmentation | |
| Hu | Multi-texture GAN: exploring the multi-scale texture translation for brain MR images | |
| Lei et al. | Generative adversarial networks for medical image synthesis | |
| Song et al. | Longitudinal structural MRI data prediction in nondemented and demented older adults via generative adversarial convolutional network | |
| Bdair et al. | Medical image-to-image translation with spatial self-attention for radiotherapy in federated learning | |
| Mansouri Musolu et al. | Deep learning and its applications in medical imaging | |
| Aderghal | Classification of multimodal MRI images using Deep Learning: Application to the diagnosis of Alzheimer’s disease. |