ResectVol: A tool to automatically segment and characterize lacunas in brain images

Abstract Objective To assess and validate the performance of a new tool developed for segmenting and characterizing lacunas in postoperative MR images of epilepsy patients. Methods A MATLAB‐based pipeline was implemented using SPM12 to produce the 3D mask of the surgical lacuna and estimate its volu...

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Autores principales:	Raphael F. Casseb, Brunno M. deCampos, Marcia Morita‐Sherman, Amr Morsi, Efstathios Kondylis, William E. Bingaman, Stephen E. Jones, Lara Jehi, Fernando Cendes
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	automatic segmentation Epilepsy MRI surgical outcome volumetry Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/87b07b439be54346b9b871bffeea5656
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spelling	oai:doaj.org-article:87b07b439be54346b9b871bffeea56562021-12-01T06:09:19ZResectVol: A tool to automatically segment and characterize lacunas in brain images2470-923910.1002/epi4.12546https://doaj.org/article/87b07b439be54346b9b871bffeea56562021-12-01T00:00:00Zhttps://doi.org/10.1002/epi4.12546https://doaj.org/toc/2470-9239Abstract Objective To assess and validate the performance of a new tool developed for segmenting and characterizing lacunas in postoperative MR images of epilepsy patients. Methods A MATLAB‐based pipeline was implemented using SPM12 to produce the 3D mask of the surgical lacuna and estimate its volume. To validate its performance, we compared the manual and automatic lacuna segmentations obtained from 51 MRI scans of epilepsy patients who underwent temporal lobe resections. Results The code is consolidated as a tool named ResectVol, which can be run via a graphical user interface or command line. The automatic and manual segmentation comparison resulted in a median Dice similarity coefficient of 0.77 (interquartile range: 0.71‐0.81). Significance Epilepsy surgery is the treatment of choice for pharmacoresistant focal epilepsies, and despite the extensive literature on the subject, we still cannot predict surgical outcomes accurately. As the volume and location of the resected tissue are fundamentally relevant to this prediction, researchers commonly perform a manual segmentation of the lacuna, which presents human bias and does not provide detailed information about the structures removed. In this study, we introduce ResectVol, a user‐friendly, fully automatic tool to accomplish these tasks. This capability enables more advanced analytical techniques applied to surgical outcomes prediction, such as machine‐learning algorithms, by facilitating coregistration of the resected area and preoperative findings with other imaging modalities such as PET, SPECT, and functional MRI ResectVol is freely available at https://www.lniunicamp.com/resectvol.Raphael F. CassebBrunno M. deCamposMarcia Morita‐ShermanAmr MorsiEfstathios KondylisWilliam E. BingamanStephen E. JonesLara JehiFernando CendesWileyarticleautomatic segmentationEpilepsyMRIsurgical outcomevolumetryNeurology. Diseases of the nervous systemRC346-429ENEpilepsia Open, Vol 6, Iss 4, Pp 720-726 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	automatic segmentation Epilepsy MRI surgical outcome volumetry Neurology. Diseases of the nervous system RC346-429
spellingShingle	automatic segmentation Epilepsy MRI surgical outcome volumetry Neurology. Diseases of the nervous system RC346-429 Raphael F. Casseb Brunno M. deCampos Marcia Morita‐Sherman Amr Morsi Efstathios Kondylis William E. Bingaman Stephen E. Jones Lara Jehi Fernando Cendes ResectVol: A tool to automatically segment and characterize lacunas in brain images
description	Abstract Objective To assess and validate the performance of a new tool developed for segmenting and characterizing lacunas in postoperative MR images of epilepsy patients. Methods A MATLAB‐based pipeline was implemented using SPM12 to produce the 3D mask of the surgical lacuna and estimate its volume. To validate its performance, we compared the manual and automatic lacuna segmentations obtained from 51 MRI scans of epilepsy patients who underwent temporal lobe resections. Results The code is consolidated as a tool named ResectVol, which can be run via a graphical user interface or command line. The automatic and manual segmentation comparison resulted in a median Dice similarity coefficient of 0.77 (interquartile range: 0.71‐0.81). Significance Epilepsy surgery is the treatment of choice for pharmacoresistant focal epilepsies, and despite the extensive literature on the subject, we still cannot predict surgical outcomes accurately. As the volume and location of the resected tissue are fundamentally relevant to this prediction, researchers commonly perform a manual segmentation of the lacuna, which presents human bias and does not provide detailed information about the structures removed. In this study, we introduce ResectVol, a user‐friendly, fully automatic tool to accomplish these tasks. This capability enables more advanced analytical techniques applied to surgical outcomes prediction, such as machine‐learning algorithms, by facilitating coregistration of the resected area and preoperative findings with other imaging modalities such as PET, SPECT, and functional MRI ResectVol is freely available at https://www.lniunicamp.com/resectvol.
format	article
author	Raphael F. Casseb Brunno M. deCampos Marcia Morita‐Sherman Amr Morsi Efstathios Kondylis William E. Bingaman Stephen E. Jones Lara Jehi Fernando Cendes
author_facet	Raphael F. Casseb Brunno M. deCampos Marcia Morita‐Sherman Amr Morsi Efstathios Kondylis William E. Bingaman Stephen E. Jones Lara Jehi Fernando Cendes
author_sort	Raphael F. Casseb
title	ResectVol: A tool to automatically segment and characterize lacunas in brain images
title_short	ResectVol: A tool to automatically segment and characterize lacunas in brain images
title_full	ResectVol: A tool to automatically segment and characterize lacunas in brain images
title_fullStr	ResectVol: A tool to automatically segment and characterize lacunas in brain images
title_full_unstemmed	ResectVol: A tool to automatically segment and characterize lacunas in brain images
title_sort	resectvol: a tool to automatically segment and characterize lacunas in brain images
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/87b07b439be54346b9b871bffeea5656
work_keys_str_mv	AT raphaelfcasseb resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages AT brunnomdecampos resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages AT marciamoritasherman resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages AT amrmorsi resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages AT efstathioskondylis resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages AT williamebingaman resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages AT stephenejones resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages AT larajehi resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages AT fernandocendes resectvolatooltoautomaticallysegmentandcharacterizelacunasinbrainimages
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ResectVol: A tool to automatically segment and characterize lacunas in brain images

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