Semiautomated four-dimensional computed tomography segmentation using deformable models.
about
Esophagus segmentation from 3D CT data using skeleton prior-based graph cut.A novel flexible framework with automatic feature correspondence optimization for nonrigid registration in radiotherapy.Navigator channel adaptation to reconstruct three dimensional heart volumes from two dimensional radiotherapy planning data.Four-dimensional deformable image registration using trajectory modeling.Development of a novel post-processing treatment planning platform for 4D radiotherapyAnniversary paper: image processing and manipulation through the pages of Medical Physics.Automated contour mapping using sparse volume sampling for 4D radiation therapy.Evaluation of various deformable image registration algorithms for thoracic images.Quantifying the accuracy of automated structure segmentation in 4D CT images using a deformable image registration algorithm.Comparison of intensity-modulated radiotherapy planning based on manual and automatically generated contours using deformable image registration in four-dimensional computed tomography of lung cancer patientsEvolution of surface-based deformable image registration for adaptive radiotherapy of non-small cell lung cancer (NSCLC).Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.Novel lung IMRT planning algorithms with nonuniform dose delivery strategy to account for respiratory motion.Upgrade and benchmarking of a 4D treatment planning system for scanned ion beam therapy.Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach.Accuracy and sensitivity of finite element model-based deformable registration of the prostate.Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method.Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation.Quantitative assessment of four‐dimensional computed tomography image acquisition quality.Determination of an optimal organ set to implement deformations to support four‐dimensional dose calculations in radiation therapy planning.Fully automated esophagus segmentation with a hierarchical deep learning approach
P2860
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P2860
Semiautomated four-dimensional computed tomography segmentation using deformable models.
description
2005 nî lūn-bûn
@nan
2005年の論文
@ja
2005年学术文章
@wuu
2005年学术文章
@zh-cn
2005年学术文章
@zh-hans
2005年学术文章
@zh-my
2005年学术文章
@zh-sg
2005年學術文章
@yue
2005年學術文章
@zh
2005年學術文章
@zh-hant
name
Semiautomated four-dimensional computed tomography segmentation using deformable models.
@en
type
label
Semiautomated four-dimensional computed tomography segmentation using deformable models.
@en
prefLabel
Semiautomated four-dimensional computed tomography segmentation using deformable models.
@en
P2093
P2860
P356
P1433
P1476
Semiautomated four-dimensional computed tomography segmentation using deformable models.
@en
P2093
Craig W Stevens
Dustin Ragan
George Starkschall
Michael Kaus
Thomas Guerrero
Todd McNutt
P2860
P304
P356
10.1118/1.1929207
P407
P577
2005-07-01T00:00:00Z