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The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical standard.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using high-quality software available for academic purposes, we ran a comparative study of manual versus semi-automated segmentation of VS on MRI with 5 clinicians and scientists. We gathered both quantitative and qualitative data to compare the two approaches; including segmentation time, segmentation effort and segmentation accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We found that the selected semi-automated segmentation approach is significantly faster (167\u00a0s vs 479\u00a0s,<jats:inline-formula><jats:alternatives><jats:tex-math>$$p&lt;0.001$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>p<\/mml:mi><mml:mo>&lt;<\/mml:mo><mml:mn>0.001<\/mml:mn><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>), less temporally and physically demanding and has approximately equal performance when compared with manual segmentation, with some improvements in accuracy. There were some limitations, including algorithmic unpredictability and error, which produced more frustration and increased mental effort in comparison with manual segmentation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We suggest that semi-automated segmentation could be applied clinically for volumetric measurement of VS on MRI. In future, the generic software could be refined for use specifically for VS segmentation, thereby improving accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s11548-020-02222-y","type":"journal-article","created":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T13:03:40Z","timestamp":1594904620000},"page":"1445-1455","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Manual segmentation versus semi-automated segmentation for\u00a0quantifying vestibular schwannoma volume on MRI"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0037-4761","authenticated-orcid":false,"given":"Hari","family":"McGrath","sequence":"first","affiliation":[]},{"given":"Peichao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Reuben","family":"Dorent","sequence":"additional","affiliation":[]},{"given":"Robert","family":"Bradford","sequence":"additional","affiliation":[]},{"given":"Shakeel","family":"Saeed","sequence":"additional","affiliation":[]},{"given":"Sotirios","family":"Bisdas","sequence":"additional","affiliation":[]},{"given":"Sebastien","family":"Ourselin","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"Shapey","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Vercauteren","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,16]]},"reference":[{"key":"2222_CR1","unstructured":"Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Shinohara RT, Berger C, Ha SM, Rozycki M, Prastawa M, Alberts E, Lipkova J, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh H, Wiest R, Kirschke J, Wiestler B, et al (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv:1811.02629"},{"key":"2222_CR2","doi-asserted-by":"crossref","unstructured":"Birkbeck N, Cobzas D, Jagers M, Murtha A, Kesztyues T (2009) An interactive graph cut method for brain tumor segmentation. 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