Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI
Open accessAbstract
OBJECTIVES: Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. METHODS: We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. RESULTS: The DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE. CONCLUSIONS: The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity. KEY POINTS: • Deep learning allows for accurate meningioma detection and segmentation • Deep learning helps clinicians to assess patients with meningiomas • Meningioma monitoring and treatment planning can be improved.
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Laukamp et al. provided a model for the automated classification of grade I, grade II, and grade III meningiomas.
“Laukamp et al. [25] provided a model for the automated classification of grade I, grade II, and grade III meningiomas.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [25]
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- Authors
- Laukamp, K. R., Thiele, F., Shakirin, G., Zopfs, D., Faymonville, A., Timmer, M., Maintz, D., Perkuhn, M., & Borggrefe, J.
- Affiliations
- University Hospital Cologne (DE)
- Corresponding author
- Borggrefe, Jan
- ORCID
- Venue
- European Radiology
- Published
- 2018-06-25
- Volume
- 29
- Issue
- 1
- Pages
- 124-132
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- PMC
- PMC6291436
- OpenAlex
- W2810024032
- Cited by
- 205
- References
- 46
- Citations by year
- 2026: 15 · 2025: 14 · 2024: 23 · 2023: 26 · 2022: 43 · 2021: 36 · 2020: 23 · 2019: 20 · 2018: 3 · 2017: 1
- FWCI
- 9.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Radiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Epidemiology › Meningioma and schwannoma management
- Keywords
- meningioma, automated tumor detection, tumor segmentation, multiparametric MRI, deep learning, volumetric assessment, meningioma grading, treatment planning, cancer monitoring, DeepMedic
- MeSH terms
- Deep Learning, Aged, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Meningeal Neoplasms, Meningioma, Middle Aged
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