Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies. - Wikidata - awgldk - TriplyDB

Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies.

Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies.

http://www.wikidata.org/entity/Q37724476

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Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.Patterns of relapse and growth kinetics of surgery- and radiation-refractory meningiomas.Compressed sensing for longitudinal MRI: An adaptive-weighted approach.The effect of chemotherapy on optic pathway gliomas and their sub-components: A volumetric MR analysis study.Expertise Affects Inter-Observer Agreement at Peripheral Locations within a Brain Tumor.Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery.Intra-rater variability in low-grade glioma segmentation.

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Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies.

http://www.wikidata.org/entity/Q37724476

description

article científic

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article scientifique

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articolo scientifico

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artigo científico

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bilimsel makale

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scientific article published on May 2014

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vedecký článok

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vetenskaplig artikel

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videnskabelig artikel

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vědecký článek

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name

Semiautomatic segmentation and ...... ongitudinal brain MRI studies.

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Semiautomatic segmentation and ...... ongitudinal brain MRI studies.

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type

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Semiautomatic segmentation and ...... ongitudinal brain MRI studies.

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Semiautomatic segmentation and ...... ongitudinal brain MRI studies.

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prefLabel

Semiautomatic segmentation and ...... ongitudinal brain MRI studies.

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Semiautomatic segmentation and ...... ongitudinal brain MRI studies.

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Medical Physics

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Semiautomatic segmentation and ...... ongitudinal brain MRI studies.

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Ben Shofty

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Dafna Ben Bashat

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Daniel L Rubin

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Kristen W Yeom

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Leo Joskowicz

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Liat Ben Sira

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Lior Weizman

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Shlomi Constantini

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P2860

New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)

Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models.

Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients.

Brain MRI tissue classification based on local Markov random fields

Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme.

A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients.

Automated temporal tracking and segmentation of lymphoma on serial CT examinations.

Within-subject template estimation for unbiased longitudinal image analysis.

Automatic brain tumor segmentation by subject specific modification of atlas priors.

MONITORING SLOWLY EVOLVING TUMORS

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052303

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scholarly article

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10.1118/1.4871040

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5

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2014-05-01T00:00:00Z

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262015421

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24784396

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