Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery

  • Eli Ben Shimol
  • Leo JoskowiczEmail author
  • Ruth Eliahou
  • Yigal Shoshan
Original Article



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.


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.


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.


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.


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



This work was partially supported by Grant 53681 from the Israel Ministry of Science, Technology and Space entitled: METASEG: a new medical image segmentation paradigm for clinical decision support and big data radiology.

Compliance with ethical standards

Conflict of interest

None of the authors has any conflict of interest. The authors have no personal financial or institutional interest in any of the materials, software or devices described in this article.

Protection of human and animal rights statement

No animals or humans were involved in this research. All scans were anonymized before delivery to the researchers.


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Copyright information

© CARS 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
  2. 2.Department of RadiologyHadassah University Medical CenterEin-Karem, JerusalemIsrael
  3. 3.Department of NeurosurgeryHadassah University Medical CenterEin-Karem, JerusalemIsrael
  4. 4.CASMIP Lab – Computer Aided Surgery and Medical Image Processing Laboratory, The Rachel and Selim Benin School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael

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