Article ; Online: A semi-automatic segmentation method for meningioma developed using a variational approach model.
2023 Volume 37, Issue 2, Page(s) 199–205
Abstract: Background: Meningioma is the commonest primary brain tumour. Volumetric post-contrast magnetic resonance imaging (MRI) is recognised as gold standard for delineation of meningioma volume but is hindered by manual processing times. We aimed to ... ...
Abstract | Background: Meningioma is the commonest primary brain tumour. Volumetric post-contrast magnetic resonance imaging (MRI) is recognised as gold standard for delineation of meningioma volume but is hindered by manual processing times. We aimed to investigate the utility of a model-based variational approach in segmenting meningioma. Methods: A database of patients with a meningioma (2007-2015) was queried for patients with a contrast-enhanced volumetric MRI, who had consented to a research tissue biobank. Manual segmentation by a neuroradiologist was performed and results were compared to the mathematical model, using a battery of tests including the Sørensen-Dice coefficient (DICE) and JACCARD index. A publicly available meningioma dataset (708 segmented T1 contrast-enhanced slices) was also used to test the reliability of the model. Results: 49 meningioma cases were included. The most common meningioma location was convexity ( Conclusions: Segmentation of meningioma volume using the proposed mathematical model was possible with accurate results. Application of this model on contrast-enhanced volumetric imaging may help reduce work burden on neuroradiologists with the increasing number in meningioma diagnoses. |
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MeSH term(s) | Humans ; Meningioma/diagnostic imaging ; Reproducibility of Results ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Meningeal Neoplasms/diagnostic imaging |
Language | English |
Publishing date | 2023-12-26 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 2257770-1 |
ISSN | 2385-1996 ; 1971-4009 ; 1120-9976 |
ISSN (online) | 2385-1996 |
ISSN | 1971-4009 ; 1120-9976 |
DOI | 10.1177/19714009231224442 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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