Ren et al., 2018 - Google Patents
Regression convolutional neural network for automated pediatric bone age assessment from hand radiographRen et al., 2018
- Document ID
- 15777780050957841161
- Author
- Ren X
- Li T
- Yang X
- Wang S
- Ahmad S
- Xiang L
- Stone S
- Li L
- Zhan Y
- Shen D
- Wang Q
- Publication year
- Publication venue
- IEEE journal of biomedical and health informatics
External Links
Snippet
Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive, and often subject to inter-observer variability …
- 210000000988 Bone and Bones 0 title abstract description 117
Classifications
-
- 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
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- 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
- 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
-
- 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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- 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
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
-
- 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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
-
- 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
- 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
- G06K2209/05—Recognition of patterns in medical or anatomical images
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ren et al. | Regression convolutional neural network for automated pediatric bone age assessment from hand radiograph | |
Deniz et al. | Segmentation of the proximal femur from MR images using deep convolutional neural networks | |
Wang et al. | An ensemble-based densely-connected deep learning system for assessment of skeletal maturity | |
Bhalodia et al. | DeepSSM: A blueprint for image-to-shape deep learning models | |
Maity et al. | Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays | |
Souadih et al. | Automatic forensic identification using 3D sphenoid sinus segmentation and deep characterization | |
Zhou et al. | External attention assisted multi-phase splenic vascular injury segmentation with limited data | |
Rodríguez et al. | Computer aided detection and diagnosis in medical imaging: a review of clinical and educational applications | |
Mihail et al. | Automatic hand skeletal shape estimation from radiographs | |
Zeng et al. | [Retracted] Multicentre Study Using Machine Learning Methods in Clinical Diagnosis of Knee Osteoarthritis | |
Peña-Solórzano et al. | Findings from machine learning in clinical medical imaging applications–Lessons for translation to the forensic setting | |
Patel | An overview and application of deep convolutional neural networks for medical image segmentation | |
Thangam et al. | Skeletal Bone Age Assessment-Research Directions. | |
Singh et al. | Attention-guided residual W-Net for supervised cardiac magnetic resonance imaging segmentation | |
Öztürk | Convolutional neural networks for medical image processing applications | |
Singh et al. | Semantic segmentation of bone structures in chest X-rays including unhealthy radiographs: A robust and accurate approach | |
Zhang et al. | Ultra-attention: automatic recognition of liver ultrasound standard sections based on visual attention perception structures | |
Li et al. | MsgeCNN: Multiscale geometric embedded convolutional neural network for ONFH segmentation and grading | |
Mostafa et al. | Hybridization between deep learning algorithms and neutrosophic theory in medical image processing: A survey | |
Navale et al. | DWT textural feature-based classification of osteoarthritis using knee X-ray images | |
Tam | Machine learning towards general medical image segmentation | |
Patel et al. | AI approaches for breast cancer diagnosis: a comprehensive study | |
Mary et al. | Survey on Segmentation Techniques for Spinal Cord Images | |
Elgohary et al. | A CAD System for Lung Cancer Detection Using Chest X-ray: A Review | |
Chen et al. | Adaptive Critical Region Extraction Net via relationship modeling for bone age assessment |