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Automated Meningioma Segmentation in Multiparametric MRI

Comparable Effectiveness of a Deep Learning Model and Manual Segmentation

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Abstract

Purpose

Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation.

Methods

The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus.

Results

Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume.

Conclusion

Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

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Abbreviations

FLAIR:

Fluid-attenuated inversion recovery

T1CE:

T1-weighted gadolinium contrast enhanced

WHO:

World Health Organization

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Author information

Authors and Affiliations

  1. Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany

    Kai Roman Laukamp, Lenhard Pennig, Frank Thiele, Robert Reimer, Georgy Shakirin, David Zopfs, Michael Perkuhn & Jan Borggrefe

  2. Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA

    Kai Roman Laukamp

  3. Department of Radiology, Case Western Reserve University Cleveland, Cleveland, OH, USA

    Kai Roman Laukamp

  4. Philips GmbH Innovative Technologies, Aachen, Germany

    Frank Thiele, Georgy Shakirin & Michael Perkuhn

  5. Center for Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

    Lukas Görtz & Marco Timmer

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  1. Kai Roman Laukamp
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Corresponding author

Correspondence to Kai Roman Laukamp.

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Conflict of interest

G. Shakirin, F. Thiele, M. Perkuhn are Philips employees. J. Borggrefe received an honorarium from Philips for scientific lectures. K.R. Laukamp, L. Pennig, R. Reimer, L. Goertz, D. Zopfs and M. Timmer declare that they have no competing interests.

Additional information

Patient population

Of the 126 patients included in this study 56 have been analyzed previously by this group and published in 2018 in European Radiology, where they also served as segmentation ground truth. To improve comparability between the deep learning models used in this study and in order to determine whether retraining by a specific tumor type is warranted, the former cohort was used again as segmentation ground truth.

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Laukamp, K.R., Pennig, L., Thiele, F. et al. Automated Meningioma Segmentation in Multiparametric MRI. Clin Neuroradiol 31, 357–366 (2021). https://doi.org/10.1007/s00062-020-00884-4

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  • DOI: https://doi.org/10.1007/s00062-020-00884-4

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