Zheng et al., 2019 - Google Patents
Coordinate-guided U-Net for automated breast segmentation on MRI imagesZheng et al., 2019
- Document ID
- 16861203998946767095
- Author
- Zheng X
- Liu Z
- Chang L
- Long W
- Lu Y
- Publication year
- Publication venue
- Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
External Links
Snippet
Magnetic resonance imaging (MRI) plays an important role in breast cancer detection and diagnosis. Breast region segmentation on MRI images is an essential step for many analysis tasks such as the assessment of background parenchymal enhancement (BPE), the analysis …
- 210000000481 Breast 0 title abstract description 65
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
- G06T2207/30—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
- 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
- G06T2207/10—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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
- 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
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dalmış et al. | Fully automated detection of breast cancer in screening MRI using convolutional neural networks | |
| Antropova et al. | Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks | |
| Clark et al. | Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks | |
| Niaf et al. | Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI | |
| Vadmal et al. | MRI image analysis methods and applications: an algorithmic perspective using brain tumors as an exemplar | |
| Sorace et al. | Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial | |
| Kwon et al. | Classification of suspicious lesions on prostate multiparametric MRI using machine learning | |
| Makni et al. | Zonal segmentation of prostate using multispectral magnetic resonance images | |
| Goyal et al. | Skin lesion boundary segmentation with fully automated deep extreme cut methods | |
| Bouget et al. | Fast meningioma segmentation in T1-weighted magnetic resonance imaging volumes using a lightweight 3D deep learning architecture | |
| Litjens et al. | Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI | |
| Fuchigami et al. | A hyperacute stroke segmentation method using 3D U-Net integrated with physicians’ knowledge for NCCT | |
| Antropova et al. | Recurrent neural networks for breast lesion classification based on DCE-MRIs | |
| Zheng et al. | Coordinate-guided U-Net for automated breast segmentation on MRI images | |
| Heidari et al. | A new case-based CAD scheme using a hierarchical SSIM feature extraction method to classify between malignant and benign cases | |
| Chen et al. | Automatic PET cervical tumor segmentation by deep learning with prior information | |
| Zhang et al. | Fully automated tumor localization and segmentation in breast DCEMRI using deep learning and kinetic prior | |
| Rampun et al. | Computer‐aided diagnosis: detection and localization of prostate cancer within the peripheral zone | |
| Shahedi et al. | A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics | |
| Martel et al. | Breast segmentation in MRI using Poisson surface reconstruction initialized with random forest edge detection | |
| Samala et al. | Homogenization of breast MRI across imaging centers and feature analysis using unsupervised deep embedding | |
| Lee et al. | Construction of a multi-phase contrast computed tomography kidney atlas | |
| Cha et al. | Computer-aided detection of bladder masses in CT urography (CTU) | |
| Zhang et al. | 3D segmentation of masses in DCE-MRI images using FCM and adaptive MRF | |
| Cha et al. | Comparison of bladder segmentation using deep-learning convolutional neural network with and without level sets |