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

Iyer et al., 2023 - Google Patents

Mesh2ssm: From surface meshes to statistical shape models of anatomy

Iyer et al., 2023

View PDF
Document ID
2851153151250071752
Author
Iyer K
Elhabian S
Publication year
Publication venue
International Conference on Medical Image Computing and Computer-Assisted Intervention

External Links

Snippet

Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population. The …
Continue reading at pmc.ncbi.nlm.nih.gov (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • G06T2207/30048Heart; Cardiac
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10072Tomographic images
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects

Similar Documents

Publication Publication Date Title
Mahapatra et al. Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network
Aviles-Rivero et al. GraphX^\small NET-NET-Chest X-Ray Classification Under Extreme Minimal Supervision
Hussein et al. Risk stratification of lung nodules using 3D CNN-based multi-task learning
Korez et al. Model-based segmentation of vertebral bodies from MR images with 3D CNNs
Vakalopoulou et al. Atlasnet: Multi-atlas non-linear deep networks for medical image segmentation
Balu et al. A deep learning framework for design and analysis of surgical bioprosthetic heart valves
Mahapatra et al. Joint registration and segmentation of xray images using generative adversarial networks
Shen et al. Region-specific diffeomorphic metric mapping
Spiegel et al. Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration
Ahmed et al. A comprehensive survey on bone segmentation techniques in knee osteoarthritis research: from conventional methods to deep learning
Cui et al. Artificial intelligence in spinal imaging: current status and future directions
Ma et al. PialNN: A fast deep learning framework for cortical pial surface reconstruction
Iyer et al. Mesh2ssm: From surface meshes to statistical shape models of anatomy
Bijar et al. Atlas-based automatic generation of subject-specific finite element tongue meshes
Khan et al. Segmentation of shoulder muscle MRI using a new region and edge based deep auto-encoder
Maes et al. The role of medical image computing and machine learning in healthcare
Hanik et al. Nonlinear regression on manifolds for shape analysis using intrinsic Bézier splines
Gaggion et al. Hybrid graph convolutional neural networks for landmark-based anatomical segmentation
Bastian et al. S3m: scalable statistical shape modeling through unsupervised correspondences
Adams et al. Can point cloud networks learn statistical shape models of anatomies?
Iyer et al. Scorp: statistics-informed dense correspondence prediction directly from unsegmented medical images
Anas et al. Ct scan registration with 3d dense motion field estimation using lsgan
Korez et al. Intervertebral disc segmentation in MR images with 3D convolutional networks
Xu et al. Image2ssm: Reimagining statistical shape models from images with radial basis functions
Kuang et al. Spinegem: A hybrid-supervised model generation strategy enabling accurate spine disease classification with a small training dataset