Cargando…

Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab

Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. Pediatric images usually have more motion artifact than adult images. The artifact can cause visible errors in brain segmentation, and one way to address it is to manually edit the segmented im...

Descripción completa

Detalles Bibliográficos
Autores principales: Pulli, Elmo P., Silver, Eero, Kumpulainen, Venla, Copeland, Anni, Merisaari, Harri, Saunavaara, Jani, Parkkola, Riitta, Lähdesmäki, Tuire, Saukko, Ekaterina, Nolvi, Saara, Kataja, Eeva-Leena, Korja, Riikka, Karlsson, Linnea, Karlsson, Hasse, Tuulari, Jetro J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108497/
https://www.ncbi.nlm.nih.gov/pubmed/35585923
http://dx.doi.org/10.3389/fnins.2022.874062
_version_ 1784708717990641664
author Pulli, Elmo P.
Silver, Eero
Kumpulainen, Venla
Copeland, Anni
Merisaari, Harri
Saunavaara, Jani
Parkkola, Riitta
Lähdesmäki, Tuire
Saukko, Ekaterina
Nolvi, Saara
Kataja, Eeva-Leena
Korja, Riikka
Karlsson, Linnea
Karlsson, Hasse
Tuulari, Jetro J.
author_facet Pulli, Elmo P.
Silver, Eero
Kumpulainen, Venla
Copeland, Anni
Merisaari, Harri
Saunavaara, Jani
Parkkola, Riitta
Lähdesmäki, Tuire
Saukko, Ekaterina
Nolvi, Saara
Kataja, Eeva-Leena
Korja, Riikka
Karlsson, Linnea
Karlsson, Hasse
Tuulari, Jetro J.
author_sort Pulli, Elmo P.
collection PubMed
description Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. Pediatric images usually have more motion artifact than adult images. The artifact can cause visible errors in brain segmentation, and one way to address it is to manually edit the segmented images. Variability in editing and quality control protocols may complicate comparisons between studies. In this article, we describe in detail the semiautomated segmentation and quality control protocol of structural brain images that was used in FinnBrain Birth Cohort Study and relies on the well-established FreeSurfer v6.0 and ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium tools. The participants were typically developing 5-year-olds [n = 134, 5.34 (SD 0.06) years, 62 girls]. Following a dichotomous quality rating scale for inclusion and exclusion of images, we explored the quality on a region of interest level to exclude all regions with major segmentation errors. The effects of manual edits on cortical thickness values were relatively minor: less than 2% in all regions. Supplementary Material cover registration and additional edit options in FreeSurfer and comparison to the computational anatomy toolbox (CAT12). Overall, we conclude that despite minor imperfections FreeSurfer can be reliably used to segment cortical metrics from T1-weighted images of 5-year-old children with appropriate quality assessment in place. However, custom templates may be needed to optimize the results for the subcortical areas. Through visual assessment on a level of individual regions of interest, our semiautomated segmentation protocol is hopefully helpful for investigators working with similar data sets, and for ensuring high quality pediatric neuroimaging data.
format Online
Article
Text
id pubmed-9108497
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-91084972022-05-17 Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab Pulli, Elmo P. Silver, Eero Kumpulainen, Venla Copeland, Anni Merisaari, Harri Saunavaara, Jani Parkkola, Riitta Lähdesmäki, Tuire Saukko, Ekaterina Nolvi, Saara Kataja, Eeva-Leena Korja, Riikka Karlsson, Linnea Karlsson, Hasse Tuulari, Jetro J. Front Neurosci Neuroscience Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. Pediatric images usually have more motion artifact than adult images. The artifact can cause visible errors in brain segmentation, and one way to address it is to manually edit the segmented images. Variability in editing and quality control protocols may complicate comparisons between studies. In this article, we describe in detail the semiautomated segmentation and quality control protocol of structural brain images that was used in FinnBrain Birth Cohort Study and relies on the well-established FreeSurfer v6.0 and ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium tools. The participants were typically developing 5-year-olds [n = 134, 5.34 (SD 0.06) years, 62 girls]. Following a dichotomous quality rating scale for inclusion and exclusion of images, we explored the quality on a region of interest level to exclude all regions with major segmentation errors. The effects of manual edits on cortical thickness values were relatively minor: less than 2% in all regions. Supplementary Material cover registration and additional edit options in FreeSurfer and comparison to the computational anatomy toolbox (CAT12). Overall, we conclude that despite minor imperfections FreeSurfer can be reliably used to segment cortical metrics from T1-weighted images of 5-year-old children with appropriate quality assessment in place. However, custom templates may be needed to optimize the results for the subcortical areas. Through visual assessment on a level of individual regions of interest, our semiautomated segmentation protocol is hopefully helpful for investigators working with similar data sets, and for ensuring high quality pediatric neuroimaging data. Frontiers Media S.A. 2022-05-02 /pmc/articles/PMC9108497/ /pubmed/35585923 http://dx.doi.org/10.3389/fnins.2022.874062 Text en Copyright © 2022 Pulli, Silver, Kumpulainen, Copeland, Merisaari, Saunavaara, Parkkola, Lähdesmäki, Saukko, Nolvi, Kataja, Korja, Karlsson, Karlsson and Tuulari. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Pulli, Elmo P.
Silver, Eero
Kumpulainen, Venla
Copeland, Anni
Merisaari, Harri
Saunavaara, Jani
Parkkola, Riitta
Lähdesmäki, Tuire
Saukko, Ekaterina
Nolvi, Saara
Kataja, Eeva-Leena
Korja, Riikka
Karlsson, Linnea
Karlsson, Hasse
Tuulari, Jetro J.
Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab
title Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab
title_full Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab
title_fullStr Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab
title_full_unstemmed Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab
title_short Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab
title_sort feasibility of freesurfer processing for t1-weighted brain images of 5-year-olds: semiautomated protocol of finnbrain neuroimaging lab
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108497/
https://www.ncbi.nlm.nih.gov/pubmed/35585923
http://dx.doi.org/10.3389/fnins.2022.874062
work_keys_str_mv AT pullielmop feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT silvereero feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT kumpulainenvenla feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT copelandanni feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT merisaariharri feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT saunavaarajani feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT parkkolariitta feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT lahdesmakituire feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT saukkoekaterina feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT nolvisaara feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT katajaeevaleena feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT korjariikka feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT karlssonlinnea feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT karlssonhasse feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab
AT tuularijetroj feasibilityoffreesurferprocessingfort1weightedbrainimagesof5yearoldssemiautomatedprotocoloffinnbrainneuroimaginglab