Semiautomatic segmentation [Neurosurgery Wiki]

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====== Semiautomatic segmentation ======

In one kind of [[segmentation]], the user outlines the [[region of interest]] with the mouse clicks and algorithms are applied so that the path that best fits the edge of the image is shown.
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The semi-automatic volumetric analysis showed a high [[interobserver]] agreement and should, therefore, be considered the [[gold standard]] for the assessment of [[EOR]]. The introduction of [[fluorescence]] has resulted in better [[resection]]s 
((Eljamel S (2015) 5-ALA fluorescence image guided resection of
glioblastoma multiforme: a meta-analysis of the literature. Int J Mol
Sci 16:10443–10456. https://doi.org/10.3390/ijms160510443)).
Sezer et al. found it to increase the accuracy of the surgeons’ estimate of [[fluorescence]], whilst resulting in a tendency towards overestimation.
Even though surgeons’ estimate of the [[extent of resection]] has clearly improved since the report of Albert et al., the reliability of their estimation is statistically moderate. Therefore, early post-operative MRI scanning for evaluation of [[EOR]] remains paramount
((Sezer S, van Amerongen MJ, Delye HHK, Ter Laan M. Accuracy of the
neurosurgeons estimation of Glioblastoma extent of resection. Acta Neurochir
(Wien). 2020 Feb;162(2):373-378. doi: 10.1007/s00701-019-04089-8. Epub 2019 Oct
28. PubMed PMID: 31656985.
)).