Literature DB >> 11161183

Automated segmentation of MR images of brain tumors.

M R Kaus1, S K Warfield, A Nabavi, P M Black, F A Jolesz, R Kikinis.   

Abstract

An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. The automated method (operator time, 5-10 minutes) allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation (operator time, 3-5 hours), making automated segmentation practical for low-grade gliomas and meningiomas.

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Year:  2001        PMID： 11161183     DOI： 10.1148/radiology.218.2.r01fe44586

Source DB:  PubMed          Journal:  Radiology        ISSN： 0033-8419            Impact factor:   11.105


  62 in total

1.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation.

Authors:  Simon K Warfield; Kelly H Zou; William M Wells
Journal:  IEEE Trans Med Imaging       Date:  2004-07       Impact factor: 10.048

2.  A statistically based flow for image segmentation.

Authors:  Eric Pichon; Allen Tannenbaum; Ron Kikinis
Journal:  Med Image Anal       Date:  2004-09       Impact factor: 8.545

3.  Segmentation of image ensembles via latent atlases.

Authors:  Tammy Riklin-Raviv; Koen Van Leemput; Bjoern H Menze; William M Wells; Polina Golland
Journal:  Med Image Anal       Date:  2010-06-04       Impact factor: 8.545

4.  Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract-Specific Effects.

Authors:  Angela Albi; Antonio Meola; Fan Zhang; Pegah Kahali; Laura Rigolo; Chantal M W Tax; Pelin Aksit Ciris; Walid I Essayed; Prashin Unadkat; Isaiah Norton; Yogesh Rathi; Olutayo Olubiyi; Alexandra J Golby; Lauren J O'Donnell
Journal:  J Neuroimaging       Date:  2018-01-10       Impact factor: 2.486

5.  Prognostic Value of Dynamic Susceptibility Contrast-Enhanced and Diffusion-Weighted MR Imaging in Patients with Glioblastomas.

Authors:  G Çoban; S Mohan; F Kural; S Wang; D M O'Rourke; H Poptani
Journal:  AJNR Am J Neuroradiol       Date:  2015-04-02       Impact factor: 3.825

6.  Semi-automatic segmentation software for quantitative clinical brain glioblastoma evaluation.

Authors:  Ying Zhu; Geoffrey S Young; Zhong Xue; Raymond Y Huang; Hui You; Kian Setayesh; Hiroto Hatabu; Fei Cao; Stephen T Wong
Journal:  Acad Radiol       Date:  2012-05-15       Impact factor: 3.173

7.  Segmentation of malignant gliomas through remote collaboration and statistical fusion.

Authors:  Zhoubing Xu; Andrew J Asman; Eesha Singh; Lola Chambless; Reid Thompson; Bennett A Landman
Journal:  Med Phys       Date:  2012-10       Impact factor: 4.071

8.  Inferring Generative Model Structure with Static Analysis.

Authors:  Paroma Varma; Bryan He; Payal Bajaj; Imon Banerjee; Nishith Khandwala; Daniel L Rubin; Christopher Ré
Journal:  Adv Neural Inf Process Syst       Date:  2017-12

9.  Three validation metrics for automated probabilistic image segmentation of brain tumours.

Authors:  Kelly H Zou; William M Wells; Ron Kikinis; Simon K Warfield
Journal:  Stat Med       Date:  2004-04-30       Impact factor: 2.373

10.  Impact of perfusion map analysis on early survival prediction accuracy in glioma patients.

Authors:  Benjamin Lemasson; Thomas L Chenevert; Theodore S Lawrence; Christina Tsien; Pia C Sundgren; Charles R Meyer; Larry Junck; Jennifer Boes; Stefanie Galbán; Timothy D Johnson; Alnawaz Rehemtulla; Brian D Ross; Craig J Galbán
Journal:  Transl Oncol       Date:  2013-12-01       Impact factor: 4.243
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