Skip to main content
Top
Published in: International Journal of Computer Assisted Radiology and Surgery 2/2018

01-02-2018 | Original Article

Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery

Authors: Eli Ben Shimol, Leo Joskowicz, Ruth Eliahou, Yigal Shoshan

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 2/2018

Login to get access

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\(^{3}\) yield a Dice coefficient of \(87.0\, \pm \, 6.2\)% 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.
Literature
1.
go back to reference Ostrom QT, Gittleman H, Liao P, Rouse C, Chen Y, Dowling J, Wolinsky Y, Kruchko C, Barnholtz-Sloan J (2014) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007–2011. Neuro Oncol 16:iv1–iv63. doi:10.1093/neuonc/nou223 CrossRefPubMedPubMedCentral Ostrom QT, Gittleman H, Liao P, Rouse C, Chen Y, Dowling J, Wolinsky Y, Kruchko C, Barnholtz-Sloan J (2014) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007–2011. Neuro Oncol 16:iv1–iv63. doi:10.​1093/​neuonc/​nou223 CrossRefPubMedPubMedCentral
5.
6.
go back to reference Barajas RF, Chang JS, Sneed PK, Segal MR, McDermott MW, Cha S (2009) Distinguishing recurrent intra-axial metastatic tumor from radiation necrosis following gamma knife radiosurgery using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. AJNR Am J Neuroradiol. doi:10.3174/ajnr.A1362 PubMedCentral Barajas RF, Chang JS, Sneed PK, Segal MR, McDermott MW, Cha S (2009) Distinguishing recurrent intra-axial metastatic tumor from radiation necrosis following gamma knife radiosurgery using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. AJNR Am J Neuroradiol. doi:10.​3174/​ajnr.​A1362 PubMedCentral
9.
go back to reference Kaley T, Barani I, Chamberlain M, McDermott M, Panageas K, Raizer J (2014) Historical benchmarks for medical therapy trials in surgery- and radiation-refractory meningioma: a RANO review. Neuro Oncol 16:829–840CrossRefPubMedPubMedCentral Kaley T, Barani I, Chamberlain M, McDermott M, Panageas K, Raizer J (2014) Historical benchmarks for medical therapy trials in surgery- and radiation-refractory meningioma: a RANO review. Neuro Oncol 16:829–840CrossRefPubMedPubMedCentral
10.
go back to reference Miller AB, Hoogstraten B, Staquet M, Winkler A (2011) Reporting results of cancer treatment. Cancer 47(1):207–214CrossRef Miller AB, Hoogstraten B, Staquet M, Winkler A (2011) Reporting results of cancer treatment. Cancer 47(1):207–214CrossRef
15.
go back to reference Liu T, Xu H, Jin W, Liu Z, Zhao Y, Tian W (2014) Medical image segmentation based on a hybrid region-based active contour model. Comput Math Methods Med. doi:10.1155/2014/890725 Liu T, Xu H, Jin W, Liu Z, Zhao Y, Tian W (2014) Medical image segmentation based on a hybrid region-based active contour model. Comput Math Methods Med. doi:10.​1155/​2014/​890725
19.
go back to reference Menze B, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Reyes M, Leemput Van (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024CrossRefPubMed Menze B, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Reyes M, Leemput Van (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024CrossRefPubMed
22.
go back to reference Sanjuan A, Price CJ, Mancini L, Josse G, Grogan A, Yamamoto AK, Geva S, Leff AP, Yousry TA, Seghier ML (2013) Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors. Front Neurosci. doi:10.3389/fnins.2013.00241 PubMedPubMedCentral Sanjuan A, Price CJ, Mancini L, Josse G, Grogan A, Yamamoto AK, Geva S, Leff AP, Yousry TA, Seghier ML (2013) Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors. Front Neurosci. doi:10.​3389/​fnins.​2013.​00241 PubMedPubMedCentral
26.
go back to reference Paragios N, Chen Y, Faugeras O (2005) Handbook of mathematical models in computer vision. Section on boundary extraction, segmentation, and grouping. Springer, vol XXXIII, 605. doi:10.1007/0-387-28831-7 Paragios N, Chen Y, Faugeras O (2005) Handbook of mathematical models in computer vision. Section on boundary extraction, segmentation, and grouping. Springer, vol XXXIII, 605. doi:10.​1007/​0-387-28831-7
28.
go back to reference Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 430–436. doi:10.1109/CVPR.2005.213 Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 430–436. doi:10.​1109/​CVPR.​2005.​213
31.
go back to reference Li C, Kao CY, Gore JC, Ding Z (2007) Implicit active contours driven by local binary fitting energy. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. doi:10.1109/CVPR.2007.383014 Li C, Kao CY, Gore JC, Ding Z (2007) Implicit active contours driven by local binary fitting energy. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. doi:10.​1109/​CVPR.​2007.​383014
35.
go back to reference Mumford D, Shah JM (1985) Boundary detection by minimizing functionals. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 137–154 Mumford D, Shah JM (1985) Boundary detection by minimizing functionals. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 137–154
36.
go back to reference Weizman L, Ben-Sira L, Joskowicz L, Precel R, Constantini S, Ben-Bashat D (2010) Automatic segmentation and components classification of optic pathway gliomas in MRI. Lecture notes in computer science, pp 103–110. doi:10.1007/978-3-642-15705-9_13 Weizman L, Ben-Sira L, Joskowicz L, Precel R, Constantini S, Ben-Bashat D (2010) Automatic segmentation and components classification of optic pathway gliomas in MRI. Lecture notes in computer science, pp 103–110. doi:10.​1007/​978-3-642-15705-9_​13
38.
go back to reference Weizman L, Ben Sira L, Joskowicz L, Rubin DL, Yeom KW, Constantini S, Shofty B, Ben Bashat D (2014) Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies. Med Phys 41:52303. doi:10.1118/1.4871040 CrossRef Weizman L, Ben Sira L, Joskowicz L, Rubin DL, Yeom KW, Constantini S, Shofty B, Ben Bashat D (2014) Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies. Med Phys 41:52303. doi:10.​1118/​1.​4871040 CrossRef
39.
go back to reference Meier R, Bauer S, Slotboom J, Wiest R, Reyes M (2014) Patient-specific semi-supervised learning for postoperative brain tumor segmentation. In: Proceedings of medical image computing and computer assisted interventions part 1, pp 714–721 Meier R, Bauer S, Slotboom J, Wiest R, Reyes M (2014) Patient-specific semi-supervised learning for postoperative brain tumor segmentation. In: Proceedings of medical image computing and computer assisted interventions part 1, pp 714–721
43.
go back to reference Senthilkumaran N, Thimmiaraja J (2014) Histogram equalization for image enhancement using MRI brain images. In: Proceedings of world congress on computing and communication technologies. doi:10.1109/WCCCT.2014.45 Senthilkumaran N, Thimmiaraja J (2014) Histogram equalization for image enhancement using MRI brain images. In: Proceedings of world congress on computing and communication technologies. doi:10.​1109/​WCCCT.​2014.​45
47.
48.
go back to reference Sagan H (2012) Introduction to the calculus of variations. Dover Books, Mineola Sagan H (2012) Introduction to the calculus of variations. Dover Books, Mineola
53.
go back to reference Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. doi:10.1109/34.232073 Huttenlocher DP, Klanderman GA, Rucklidge WJ (1993) Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. doi:10.​1109/​34.​232073
Metadata
Title
Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery
Authors
Eli Ben Shimol
Leo Joskowicz
Ruth Eliahou
Yigal Shoshan
Publication date
01-02-2018
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 2/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-017-1673-7

Other articles of this Issue 2/2018

International Journal of Computer Assisted Radiology and Surgery 2/2018 Go to the issue