Literature DB >> 15710538

A system for brain tumor volume estimation via MR imaging and fuzzy connectedness.

Jianguo Liu1, Jayaram K Udupa, Dewey Odhner, David Hackney, Gul Moonis.   

Abstract

This paper presents a method for the precise, accurate and efficient quantification of brain tumor (glioblastomas) via MRI that can be used routinely in the clinic. Tumor volume is considered useful in evaluating disease progression and response to therapy, and in assessing the need for changes in treatment plans. We use multiple MRI protocols including FLAIR, T1, and T1 with Gd enhancement to gather information about different aspects of the tumor and its vicinity. These include enhancing tissue, nonenhancing tumor, edema, and combinations of edema and tumor. We have adapted the fuzzy connectedness framework for tumor segmentation in this work and the method requires only limited user interaction in routine clinical use. The system has been tested for its precision, accuracy, and efficiency, utilizing 10 patient studies. The percent coefficient of variation (% CV) in volume due to operator subjectivity in specifying seeds for fuzzy connectedness segmentation is less than 1%. The mean operator and computer time required per study for estimating the volumes of both edema and enhancing tumor is about 16 min. The software package is designed to run under operator supervision. Delineation has been found to agree with the operators' visual inspection most of the time except in some cases when the tumor is close to the boundary of the brain. In the latter case, the scalp, surgical scar, or orbital contents are included in the delineation, and an operator has to exclude this manually. The methodology is rapid, robust, consistent, yielding highly reproducible measurements, and is likely to become part of the routine evaluation of brain tumor patients in our health system.

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Year:  2005        PMID: 15710538     DOI: 10.1016/j.compmedimag.2004.07.008

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  16 in total

1.  Can we improve accuracy and reliability of MRI interpretation in children with optic pathway glioma? Proposal for a reproducible imaging classification.

Authors:  Julien Lambron; Josué Rakotonjanahary; Didier Loisel; Eric Frampas; Emilie De Carli; Matthieu Delion; Xavier Rialland; Frédérique Toulgoat
Journal:  Neuroradiology       Date:  2015-10-30       Impact factor: 2.804

2.  MONITORING SLOWLY EVOLVING TUMORS.

Authors:  E Konukoglu; W M Wells; S Novellas; N Ayache; R Kikinis; P M Black; K M Pohl
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2008-06-13

3.  Computer assisted diagnostic system in tumor radiography.

Authors:  Ahmed Faisal; Sharmin Parveen; Shahriar Badsha; Hasan Sarwar; Ahmed Wasif Reza
Journal:  J Med Syst       Date:  2013-03-17       Impact factor: 4.460

4.  Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights.

Authors:  Yangming Ou; Hamed Akbari; Michel Bilello; Xiao Da; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2014-06-13       Impact factor: 10.048

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

Authors:  Kilian M Pohl; Ender Konukoglu; Sebastian Novellas; Nicholas Ayache; Andriy Fedorov; Ion-Florin Talos; Alexandra Golby; William M Wells; Ron Kikinis; Peter M Black
Journal:  Neurosurgery       Date:  2011-03       Impact factor: 4.654

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

Authors:  Lior Weizman; Liat Ben Sira; Leo Joskowicz; Daniel L Rubin; Kristen W Yeom; Shlomi Constantini; Ben Shofty; Dafna Ben Bashat
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

7.  Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast.

Authors:  Stefanie J C G Hectors; Igor Jacobs; Gustav J Strijkers; Klaas Nicolay
Journal:  MAGMA       Date:  2014-11-27       Impact factor: 2.310

8.  GLISTR: glioma image segmentation and registration.

Authors:  Ali Gooya; Kilian M Pohl; Michel Bilello; Luigi Cirillo; George Biros; Elias R Melhem; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2012-08-13       Impact factor: 10.048

9.  Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

Authors:  Wei Wu; Albert Y C Chen; Liang Zhao; Jason J Corso
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-07-17       Impact factor: 2.924

10.  Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.

Authors:  Nirmalya Ghosh; Yu Sun; Bir Bhanu; Stephen Ashwal; Andre Obenaus
Journal:  Med Image Anal       Date:  2014-05-16       Impact factor: 8.545

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