Cargando…

Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold

Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical recon...

Descripción completa

Detalles Bibliográficos
Autores principales: DeKraker, Jordan, Haast, Roy AM, Yousif, Mohamed D, Karat, Bradley, Lau, Jonathan C, Köhler, Stefan, Khan, Ali R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831605/
https://www.ncbi.nlm.nih.gov/pubmed/36519725
http://dx.doi.org/10.7554/eLife.77945
_version_ 1784867879102971904
author DeKraker, Jordan
Haast, Roy AM
Yousif, Mohamed D
Karat, Bradley
Lau, Jonathan C
Köhler, Stefan
Khan, Ali R
author_facet DeKraker, Jordan
Haast, Roy AM
Yousif, Mohamed D
Karat, Bradley
Lau, Jonathan C
Köhler, Stefan
Khan, Ali R
author_sort DeKraker, Jordan
collection PubMed
description Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject’s hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper, we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.
format Online
Article
Text
id pubmed-9831605
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher eLife Sciences Publications, Ltd
record_format MEDLINE/PubMed
spelling pubmed-98316052023-01-11 Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold DeKraker, Jordan Haast, Roy AM Yousif, Mohamed D Karat, Bradley Lau, Jonathan C Köhler, Stefan Khan, Ali R eLife Neuroscience Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject’s hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper, we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods. eLife Sciences Publications, Ltd 2022-12-15 /pmc/articles/PMC9831605/ /pubmed/36519725 http://dx.doi.org/10.7554/eLife.77945 Text en © 2022, DeKraker et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
DeKraker, Jordan
Haast, Roy AM
Yousif, Mohamed D
Karat, Bradley
Lau, Jonathan C
Köhler, Stefan
Khan, Ali R
Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
title Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
title_full Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
title_fullStr Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
title_full_unstemmed Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
title_short Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
title_sort automated hippocampal unfolding for morphometry and subfield segmentation with hippunfold
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831605/
https://www.ncbi.nlm.nih.gov/pubmed/36519725
http://dx.doi.org/10.7554/eLife.77945
work_keys_str_mv AT dekrakerjordan automatedhippocampalunfoldingformorphometryandsubfieldsegmentationwithhippunfold
AT haastroyam automatedhippocampalunfoldingformorphometryandsubfieldsegmentationwithhippunfold
AT yousifmohamedd automatedhippocampalunfoldingformorphometryandsubfieldsegmentationwithhippunfold
AT karatbradley automatedhippocampalunfoldingformorphometryandsubfieldsegmentationwithhippunfold
AT laujonathanc automatedhippocampalunfoldingformorphometryandsubfieldsegmentationwithhippunfold
AT kohlerstefan automatedhippocampalunfoldingformorphometryandsubfieldsegmentationwithhippunfold
AT khanalir automatedhippocampalunfoldingformorphometryandsubfieldsegmentationwithhippunfold