Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study.
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Vision 20/20: perspectives on automated image segmentation for radiotherapyMulti-modal glioblastoma segmentation: man versus machine.Making the right software choice for clinically used equipment in radiation oncology.Absolute quantitation of brain metabolites with respect to heterogeneous tissue compositions in (1)H-MR spectroscopic volumes.Iterative probabilistic voxel labeling: automated segmentation for analysis of The Cancer Imaging Archive glioblastoma images.Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI featuresFully Automated Enhanced Tumor Compartmentalization: Man vs. Machine Reloaded.Evaluation of multiple-atlas-based strategies for segmentation of the thyroid gland in head and neck CT images for IMRTA comparison of two commercial volumetry software programs in the analysis of pulmonary ground-glass nodules: segmentation capability and measurement accuracySegmentation editing improves efficiency while reducing inter-expert variation and maintaining accuracy for normal brain tissues in the presence of space-occupying lesionsQuantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials.Deformable image registration based automatic CT-to-CT contour propagation for head and neck adaptive radiotherapy in the routine clinical setting.Expertise Affects Inter-Observer Agreement at Peripheral Locations within a Brain Tumor.Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.Automatic Intracranial Segmentation: Is the Clinician Still Needed?Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.Structured Reporting in Neuroradiology: Intracranial Tumors.GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma SegmentationSegmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative FrameworkVisual Three-Dimensional Reconstruction of Aortic Dissection Based on Medical CT Images
P2860
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P2860
Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study.
description
2011 nî lūn-bûn
@nan
2011 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Comparison of manual and autom ...... lesions: a multi-expert study.
@ast
Comparison of manual and autom ...... lesions: a multi-expert study.
@en
type
label
Comparison of manual and autom ...... lesions: a multi-expert study.
@ast
Comparison of manual and autom ...... lesions: a multi-expert study.
@en
prefLabel
Comparison of manual and autom ...... lesions: a multi-expert study.
@ast
Comparison of manual and autom ...... lesions: a multi-expert study.
@en
P2093
P2860
P356
P1476
Comparison of manual and autom ...... lesions: a multi-expert study
@en
P2093
A J Cmelak
A W Malcolm
B M Dawant
David S Yu
Eddy S Yang
P2860
P304
P356
10.1088/0031-9155/56/14/021
P577
2011-07-01T00:00:00Z