Literature DB >> 29032421

Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery.

Eli Ben Shimol1, Leo Joskowicz2,3, Ruth Eliahou4, Yigal Shoshan5.   

Abstract

PURPOSE: Stereotactic radiosurgery (SRS) is a common treatment for intracranial meningiomas. SRS is planned on a pre-therapy gadolinium-enhanced T1-weighted MRI scan (Gd-T1w MRI) in which the meningioma contours have been delineated. Post-SRS therapy serial Gd-T1w MRI scans are then acquired for longitudinal treatment evaluation. Accurate tumor volume change quantification is required for treatment efficacy evaluation and for treatment continuation.
METHOD: We present a new algorithm for the automatic segmentation and volumetric assessment of meningioma in post-therapy Gd-T1w MRI scans. The inputs are the pre- and post-therapy Gd-T1w MRI scans and the meningioma delineation in the pre-therapy scan. The output is the meningioma delineations and volumes in the post-therapy scan. The algorithm uses the pre-therapy scan and its meningioma delineation to initialize an extended Chan-Vese active contour method and as a strong patient-specific intensity and shape prior for the post-therapy scan meningioma segmentation. The algorithm is automatic, obviates the need for independent tumor localization and segmentation initialization, and incorporates the same tumor delineation criteria in both the pre- and post-therapy scans.
RESULTS: Our experimental results on retrospective pre- and post-therapy scans with a total of 32 meningiomas with volume ranges 0.4-26.5 cm[Formula: see text] yield a Dice coefficient of [Formula: see text]% with respect to ground-truth delineations in post-therapy scans created by two clinicians. These results indicate a high correspondence to the ground-truth delineations.
CONCLUSION: Our algorithm yields more reliable and accurate tumor volume change measurements than other stand-alone segmentation methods. It may be a useful tool for quantitative meningioma prognosis evaluation after SRS.

Entities:  

Keywords:  Brain tumors segmentation in MRI scans; Chan–Vese segmentation method; Longitudinal stereotactic radiosurgery evaluation; Meningioma

Mesh:

Year:  2017        PMID: 29032421     DOI: 10.1007/s11548-017-1673-7

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  35 in total

1.  Influence of MRI acquisition protocols and image intensity normalization methods on texture classification.

Authors:  G Collewet; M Strzelecki; F Mariette
Journal:  Magn Reson Imaging       Date:  2004-01       Impact factor: 2.546

2.  Decoupled active contour (DAC) for boundary detection.

Authors:  Akshaya Kumar Mishra; Paul W Fieguth; David A Clausi
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2011-02       Impact factor: 6.226

3.  Automatic segmentation and components classification of optic pathway gliomas in MRI.

Authors:  Lior Weizman; Liat Ben-Sira; Leo Joskowicz; Ronit Precel; Shlomi Constantini; Dafna Ben-Bashat
Journal:  Med Image Comput Comput Assist Interv       Date:  2010

4.  Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI.

Authors:  L Weizman; L Ben Sira; L Joskowicz; S Constantini; R Precel; B Shofty; D Ben Bashat
Journal:  Med Image Anal       Date:  2011-07-21       Impact factor: 8.545

5.  Patient-specific semi-supervised learning for postoperative brain tumor segmentation.

Authors:  Raphael Meier; Stefan Bauer; Johannes Slotboom; Roland Wiest; Mauricio Reyes
Journal:  Med Image Comput Comput Assist Interv       Date:  2014

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

Review 7.  State of the art survey on MRI brain tumor segmentation.

Authors:  Nelly Gordillo; Eduard Montseny; Pilar Sobrevilla
Journal:  Magn Reson Imaging       Date:  2013-06-20       Impact factor: 2.546

8.  Volumetric follow-up of meningiomas: a quantitative method to evaluate treatment outcome of gamma knife radiosurgery.

Authors:  Guenther C Feigl; Madjid Samii; Gerhard A Horstmann
Journal:  Neurosurgery       Date:  2007-08       Impact factor: 4.654

9.  Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing.

Authors:  Thomas M Hsieh; Yi-Min Liu; Chun-Chih Liao; Furen Xiao; I-Jen Chiang; Jau-Min Wong
Journal:  BMC Med Inform Decis Mak       Date:  2011-08-26       Impact factor: 2.796

10.  Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions.

Authors:  Xiaofei Sun; Lin Shi; Yishan Luo; Wei Yang; Hongpeng Li; Peipeng Liang; Kuncheng Li; Vincent C T Mok; Winnie C W Chu; Defeng Wang
Journal:  Biomed Eng Online       Date:  2015-07-28       Impact factor: 2.819

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  2 in total

1.  Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.

Authors:  Kai Roman Laukamp; Frank Thiele; Georgy Shakirin; David Zopfs; Andrea Faymonville; Marco Timmer; David Maintz; Michael Perkuhn; Jan Borggrefe
Journal:  Eur Radiol       Date:  2018-06-25       Impact factor: 5.315

Review 2.  Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.

Authors:  Emilia Gryska; Justin Schneiderman; Isabella Björkman-Burtscher; Rolf A Heckemann
Journal:  BMJ Open       Date:  2021-01-29       Impact factor: 2.692

  2 in total

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