Meng et al., 2025 - Google Patents
Automated Measurements of Spinal Parameters for Scoliosis Using Deep LearningMeng et al., 2025
- Document ID
- 13192998450431964781
- Author
- Meng X
- Zhu S
- Yang Q
- Zhu F
- Wang Z
- Liu X
- Dong P
- Wang S
- Fan L
- Publication year
- Publication venue
- Spine
External Links
Snippet
Study Design. Retrospective single-institution study. Objective. To develop and validate an automated convolutional neural network (CNN) to measure the Cobb angle, T1 tilt angle, coronal balance, clavicular angle, height of the shoulders, T5–T12 Cobb angle, and sagittal …
- 238000005259 measurement 0 title abstract description 70
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
- 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
- G06F19/321—Management of medical image data, e.g. communication or archiving systems such as picture archiving and communication systems [PACS] or related medical protocols such as digital imaging and communications in medicine protocol [DICOM]; Editing of medical image data, e.g. adding diagnosis information
-
- 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
-
- 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
- G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
- G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane
-
- 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
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0068—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping
-
- 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
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computerised tomographs
- A61B6/032—Transmission computed tomography [CT]
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Watanabe et al. | An application of artificial intelligence to diagnostic imaging of spine disease: estimating spinal alignment from Moiré images | |
| Paik et al. | Numeric and morphological verification of lumbosacral segments in 8280 consecutive patients | |
| Hetherington et al. | SLIDE: automatic spine level identification system using a deep convolutional neural network | |
| Glaser et al. | Comparison of 3-dimensional spinal reconstruction accuracy: biplanar radiographs with EOS versus computed tomography | |
| Birchall et al. | Measurement of vertebral rotation in adolescent idiopathic scoliosis using three-dimensional magnetic resonance imaging | |
| Zhang et al. | Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip | |
| JP5337845B2 (en) | How to perform measurements on digital images | |
| Wong et al. | Continuous dynamic spinal motion analysis | |
| US8150132B2 (en) | Image analysis apparatus, image analysis method, and computer-readable recording medium storing image analysis program | |
| Sardjono et al. | Automatic Cobb angle determination from radiographic images | |
| Davis et al. | Is there asymmetry between the concave and convex pedicles in adolescent idiopathic scoliosis? A CT investigation | |
| US20110249876A1 (en) | Method of performing measurements on digital images | |
| Mok et al. | Comparison of observer variation in conventional and three digital radiographic methods used in the evaluation of patients with adolescent idiopathic scoliosis | |
| Tyrakowski et al. | Influence of pelvic rotation on pelvic incidence, pelvic tilt, and sacral slope | |
| US11468659B2 (en) | Learning support device, learning support method, learning support program, region-of-interest discrimination device, region-of-interest discrimination method, region-of-interest discrimination program, and learned model | |
| Graf et al. | Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation | |
| Van Bergen et al. | Arthroscopic accessibility of the talus quantified by computed tomography simulation | |
| Johnson et al. | Artificial intelligence to preoperatively predict proximal junction kyphosis following adult spinal deformity surgery: soft tissue imaging may be necessary for accurate models | |
| Van Ijsseldijk et al. | Three dimensional measurement of minimum joint space width in the knee from stereo radiographs using statistical shape models | |
| Pedersen et al. | Radiographic measurements in developmental dysplasia of the hip: reliability and validity of a digitizing program | |
| Zhang et al. | Deformable 3D–2D image registration and analysis of global spinal alignment in long‐length intraoperative spine imaging | |
| Adam et al. | Automatic measurement of vertebral rotation in idiopathic scoliosis | |
| Doerr et al. | Data-driven detection and registration of spine surgery instrumentation in intraoperative images | |
| Bogdanovic et al. | AI-based measurement of lumbar spinal stenosis on MRI: external evaluation of a fully automated model | |
| Amini et al. | Fully automated Region-Specific Human-Perceptive-Equivalent image quality assessment: application to 18F-FDG PET scans |