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.
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Acknowledgements
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.
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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.
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No animals or humans were involved in this research. All scans were anonymized before delivery to the researchers.
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Shimol, E.B., Joskowicz, L., Eliahou, R. et al. Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery. Int J CARS 13, 215–228 (2018). https://doi.org/10.1007/s11548-017-1673-7
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DOI: https://doi.org/10.1007/s11548-017-1673-7