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

Burrows, Liam, Patel, Jay, Islim, Abdurrahman I, Jenkinson, Michael D ORCID: 0000-0003-4587-2139, Mills, Samantha J ORCID: 0000-0002-1017-6654 and Chen, Ke ORCID: 0000-0002-6093-6623 (2023) A semi-automatic segmentation method for meningioma developed using a variational approach model. The neuroradiology journal, 37 (2). p. 19714009231224442.

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Official URL: http://dx.doi.org/10.1177/19714009231224442

Abstract

<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>49 meningioma cases were included. The most common meningioma location was convexity (<i>n</i> = 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 cm<sup>3</sup> (IQR 4.9-31.2). The median meningioma volume using the mathematical model was 16.9 cm<sup>3</sup> (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.<h4>Conclusions</h4>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.

Item Type:	Article
Uncontrolled Keywords:	Humans, Meningioma, Meningeal Neoplasms, Magnetic Resonance Imaging, Reproducibility of Results, Image Processing, Computer-Assisted
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User:	Symplectic Admin
Date Deposited:	09 Jan 2024 11:29
Last Modified:	23 Mar 2024 01:41
DOI:	10.1177/19714009231224442
Open Access URL:	https://journals.sagepub.com/doi/10.1177/197140092...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3177734