A semi-automatic segmentation method for meningioma developed using a variational approach model

Burrows, Liam and Patel, Jay and Islim, Abdurrahman I and Jenkinson, Michael D and Mills, Samantha J and Chen, Ke (2024) A semi-automatic segmentation method for meningioma developed using a variational approach model. Neuroradiology Journal, 37 (2). 199–205. ISSN 2385-1996 (https://doi.org/10.1177/19714009231224442)

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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 (n = 15, 30.6%). The mathematical model segmented all but one incidental meningioma, which failed due to the lack of contrast uptake. The median meningioma volume by manual segmentation was 19.0 cm3 (IQR 4.9–31.2). The median meningioma volume using the mathematical model was 16.9 cm3 (IQR 4.6–28.34). The mean DICE score was 0.90 (SD = 0.04). The mean JACCARD index was 0.82 (SD = 0.07). For the publicly available dataset, the mean DICE and JACCARD scores were 0.90 (SD = 0.06) and 0.82 (SD = 0.10), respectively. 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.

ORCID iDs

Burrows, Liam, Patel, Jay, Islim, Abdurrahman I, Jenkinson, Michael D, Mills, Samantha J and Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623;
CORE (COnnecting REpositories)