Towards a representative reference for MRI-based human axon radius assessment using light microscopy
•A pipeline for automated estimation of ensemble mean axon radii at MRI voxel scale•Incorporation of 4 human white matter brain tissue samples for training and testing•Accurate mapping of the MRI-visible radius in the presence of staining heterogeneity•Includes 2 to 4 orders of magnitude more axons...
Gespeichert in:
| Veröffentlicht in: | NeuroImage (Orlando, Fla.) Jg. 249; S. 118906 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Elsevier Inc
01.04.2022
Elsevier Limited Elsevier |
| Schlagworte: | |
| ISSN: | 1053-8119, 1095-9572, 1095-9572 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •A pipeline for automated estimation of ensemble mean axon radii at MRI voxel scale•Incorporation of 4 human white matter brain tissue samples for training and testing•Accurate mapping of the MRI-visible radius in the presence of staining heterogeneity•Includes 2 to 4 orders of magnitude more axons than the current gold standard (EM)•The pipeline enables validation of biophysical, MRI-based radius estimation models
Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions. |
|---|---|
| AbstractList | Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (r
). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (r
) on small ensembles of axons, it is unsuited to estimate the tail-weighted r
. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for r
. In a human corpus callosum, we assessed estimation accuracy and bias of r
and r
. Furthermore, we investigated whether mapping anatomy-related variation of r
and r
is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in r
. Compared to r
, r
was estimated with higher accuracy (maximum normalized-root-mean-square-error of r
: 8.5 %; r
: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of r
: 4.8 %; r
: 13.4 %). While r
was confounded by variation of the image intensity, variation of r
seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to r
. In conclusion, the proposed method is a step towards representatively estimating r
at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions. •A pipeline for automated estimation of ensemble mean axon radii at MRI voxel scale•Incorporation of 4 human white matter brain tissue samples for training and testing•Accurate mapping of the MRI-visible radius in the presence of staining heterogeneity•Includes 2 to 4 orders of magnitude more axons than the current gold standard (EM)•The pipeline enables validation of biophysical, MRI-based radius estimation models Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions. Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions. Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions. Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions. |
| ArticleNumber | 118906 |
| Author | Morozova, Maria Fricke, Björn Mordhorst, Laurin Jäger, Carsten Geyer, Stefan Tabarin, Thibault Morawski, Markus Rusch, Henriette Oeschger, Jan Malte Weiskopf, Nikolaus Papazoglou, Sebastian Mohammadi, Siawoosh |
| Author_xml | – sequence: 1 givenname: Laurin surname: Mordhorst fullname: Mordhorst, Laurin email: laurin.mordhorst@gmx.de organization: Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany – sequence: 2 givenname: Maria surname: Morozova fullname: Morozova, Maria organization: Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany – sequence: 3 givenname: Sebastian surname: Papazoglou fullname: Papazoglou, Sebastian organization: Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany – sequence: 4 givenname: Björn surname: Fricke fullname: Fricke, Björn organization: Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany – sequence: 5 givenname: Jan Malte surname: Oeschger fullname: Oeschger, Jan Malte organization: Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany – sequence: 6 givenname: Thibault surname: Tabarin fullname: Tabarin, Thibault organization: Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany – sequence: 7 givenname: Henriette surname: Rusch fullname: Rusch, Henriette organization: Paul Flechsig Institute of Brain Research, Medical Faculty, Leipzig University, Leipzig, Germany – sequence: 8 givenname: Carsten surname: Jäger fullname: Jäger, Carsten organization: Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany – sequence: 9 givenname: Stefan surname: Geyer fullname: Geyer, Stefan organization: Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany – sequence: 10 givenname: Nikolaus surname: Weiskopf fullname: Weiskopf, Nikolaus organization: Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany – sequence: 11 givenname: Markus surname: Morawski fullname: Morawski, Markus organization: Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany – sequence: 12 givenname: Siawoosh surname: Mohammadi fullname: Mohammadi, Siawoosh organization: Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35032659$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkc1u1DAUhSNURH_gFZAlNmwy9XUSx94gaAV0pCIkVNaWY9-ZekjswU4K8_Y4TCnSrGblH333yD7feXHig8eiIEAXQIFfbhYepxjcoNe4YJSxBYCQlD8rzoDKppRNy07mfVOVAkCeFucpbSilEmrxojitGlox3sizwt6FXzraRDSJuI2Y0I96dA-YjyuM6A2SVYjky7dl2emEltxPg_ZE_w6eRG3dlEdTwpSGPEmm5Pya9G59P5LBmRiSCdvdy-L5SvcJXz2uF8X3Tx_vrm_K26-fl9cfbkvTtM1YYget7CrUgnItmKQMjIZVm5-qoeIdWMNtzS1yzlhj2lVtaqiopYK12ImuuiiW-1wb9EZtY-4n7lTQTv29CHGtdByd6VG1dW041ayxpq4ROtl1DdZCUGCyZUBz1tt91jaGnxOmUQ0uGex77TFMSTHOKBW0ApnRNwfoJkzR55_OVAuMMVln6vUjNXUD2qfn_XORAbEH5tpSrv8JAapm7Wqj_mtXs3a1155H3x2MGjdrDH6M2vXHBFztAzDreXAYVTJulm9dRDPm_twxIe8PQkzvvDO6_4G74yL-AP_s5Ig |
| CitedBy_id | crossref_primary_10_1371_journal_pbio_3002906 crossref_primary_10_1016_j_compbiomed_2023_107617 |
| Cites_doi | 10.1007/BF00239520 10.1016/0006-8993(82)91179-9 10.1038/s42003-021-01699-w 10.1007/s00422-014-0626-2 10.1016/j.neuroimage.2014.09.006 10.1016/j.neuroimage.2019.116186 10.1002/mrm.25631 10.1016/j.media.2020.101759 10.1002/nbm.3711 10.1371/journal.pcbi.1007004 10.1016/j.neuroimage.2006.01.015 10.1007/s00429-019-01844-6 10.1016/0006-8993(92)90178-C 10.1007/s00429-014-0974-7 10.1007/s00429-014-0871-0 10.1016/j.neuroimage.2015.03.061 10.1073/pnas.2012533117 10.7554/eLife.49855 10.1002/nbm.3841 10.1002/mus.880030207 10.1038/s41598-019-42648-2 10.1002/mrm.21577 10.1016/j.neuroimage.2017.07.060 10.1038/s41598-018-22181-4 10.1016/j.neuroimage.2010.05.043 10.1016/j.neuroimage.2015.08.017 10.1073/pnas.0907655106 10.7717/peerj.453 10.1016/j.neuroimage.2015.05.023 10.1038/s42254-021-00326-1 10.1002/nbm.3462 |
| ContentType | Journal Article |
| Copyright | 2022 Copyright © 2022. Published by Elsevier Inc. Copyright Elsevier Limited Apr 1, 2022 |
| Copyright_xml | – notice: 2022 – notice: Copyright © 2022. Published by Elsevier Inc. – notice: Copyright Elsevier Limited Apr 1, 2022 |
| DBID | 6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7TK 7X7 7XB 88E 88G 8AO 8FD 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2M M7P P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ Q9U RC3 7X8 DOA |
| DOI | 10.1016/j.neuroimage.2022.118906 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Neurosciences Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Psychology Database (Alumni) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Psychology Database Biological Science Database Biotechnology and BioEngineering Abstracts Proquest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology ProQuest Central Basic Genetics Abstracts MEDLINE - Academic DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest One Psychology ProQuest Central Student Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection Genetics Abstracts Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Psychology Journals (Alumni) Biological Science Database ProQuest SciTech Collection Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest Psychology Journals ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE ProQuest One Psychology MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1095-9572 |
| ExternalDocumentID | oai_doaj_org_article_744c60a25dc44e1b9bb5e48801297210 35032659 10_1016_j_neuroimage_2022_118906 S1053811922000362 |
| Genre | Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | --- --K --M .1- .FO .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 5RE 5VS 7-5 71M 7X7 88E 8AO 8FE 8FH 8FI 8FJ 8P~ 9JM AABNK AAEDT AAEDW AAFWJ AAIKJ AAKOC AALRI AAOAW AATTM AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFNM ABFRF ABIVO ABJNI ABMAC ABMZM ABUWG ACDAQ ACGFO ACGFS ACIEU ACLOT ACPRK ACRLP ACVFH ADBBV ADCNI ADEZE ADFRT ADVLN AEBSH AEFWE AEIPS AEKER AENEX AEUPX AFJKZ AFKRA AFPKN AFPUW AFRHN AFTJW AFXIZ AGUBO AGWIK AGYEJ AHHHB AHMBA AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP AXJTR AZQEC BBNVY BENPR BHPHI BKOJK BLXMC BNPGV BPHCQ BVXVI CCPQU CS3 DM4 DU5 DWQXO EBS EFBJH EFKBS EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN FYUFA G-Q GBLVA GNUQQ GROUPED_DOAJ HCIFZ HMCUK IHE J1W KOM LG5 LK8 LX8 M1P M29 M2M M2V M41 M7P MO0 MOBAO N9A O-L O9- OAUVE OK1 OVD OZT P-8 P-9 P2P PC. PHGZM PHGZT PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PSYQQ Q38 ROL RPZ SAE SCC SDF SDG SDP SES SSH SSN SSZ T5K TEORI UKHRP UV1 YK3 Z5R ZU3 ~G- ~HD 6I. AACTN AADPK AAFTH AAIAV AAQFI ABLVK ABYKQ AFKWA AJOXV AMFUW C45 HMQ LCYCR NCXOZ SNS ZA5 29N 53G 9DU AAQXK AAYXX ABXDB ACRPL ADFGL ADMUD ADNMO ADXHL AFFHD AGHFR AGQPQ AKRLJ ASPBG AVWKF AZFZN CAG CITATION COF EFLBG EJD FEDTE FGOYB G-2 HDW HEI HMK HMO HVGLF HZ~ R2- SEW WUQ XPP ZMT 0SF ALIPV CGR CUY CVF ECM EIF NPM 3V. 7TK 7XB 8FD 8FK FR3 K9. P64 PKEHL PQEST PQUKI PRINS Q9U RC3 7X8 PUEGO |
| ID | FETCH-LOGICAL-c575t-eb179b3ea806a829021ca1f7326a136b1dc6d46de66225c7f4c4130d0827eb8b3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000766269800008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1053-8119 1095-9572 |
| IngestDate | Fri Oct 03 12:36:02 EDT 2025 Sat Sep 27 17:14:38 EDT 2025 Tue Oct 07 06:56:13 EDT 2025 Wed Feb 19 02:27:03 EST 2025 Tue Nov 18 22:36:33 EST 2025 Sat Nov 29 06:55:36 EST 2025 Fri Feb 23 02:40:58 EST 2024 Tue Oct 14 19:35:52 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Deep learning MRI-based axon radius Neuroanatomy Cross microscopy Axon radii distribution |
| Language | English |
| License | This is an open access article under the CC BY license. Copyright © 2022. Published by Elsevier Inc. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c575t-eb179b3ea806a829021ca1f7326a136b1dc6d46de66225c7f4c4130d0827eb8b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://doaj.org/article/744c60a25dc44e1b9bb5e48801297210 |
| PMID | 35032659 |
| PQID | 2627122294 |
| PQPubID | 2031077 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_744c60a25dc44e1b9bb5e48801297210 proquest_miscellaneous_2620080319 proquest_journals_2627122294 pubmed_primary_35032659 crossref_primary_10_1016_j_neuroimage_2022_118906 crossref_citationtrail_10_1016_j_neuroimage_2022_118906 elsevier_sciencedirect_doi_10_1016_j_neuroimage_2022_118906 elsevier_clinicalkey_doi_10_1016_j_neuroimage_2022_118906 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-04-01 2022-04-00 20220401 |
| PublicationDateYYYYMMDD | 2022-04-01 |
| PublicationDate_xml | – month: 04 year: 2022 text: 2022-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Amsterdam |
| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2022 |
| Publisher | Elsevier Inc Elsevier Limited Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier Limited – name: Elsevier |
| References | Andersson, Kjer, Rafael-Patino, Pacureanu, Pakkenberg, Thiran, Ptito, Bech, Bjorholm Dahl, Andersen Dahl, Dyrby (bib0007) 2020; 117 Tan, Le (bib0037) 2019 Nilsson, Lasič, Drobnjak, Topgaard, Westin (bib0030) 2017; 30 Yushkevich, Piven, Hazlett, Smith, Ho, Gee, Gerig (bib0046) 2006; 31 Lee, Yaros, Veraart, Pathan, Liang, Kim, Novikov, Fieremans (bib0025) 2019; 224 Kakkar, Bennett, Siow, Richardson, Ianuş, Quick, Atkinson, Phillips, Drobnjak (bib0022) 2018; 182 Zaimi, Wabartha, Herman, Antonsanti, Perone, Cohen-Adad (bib0047) 2018; 8 Macenko, Niethammer, Marron, Borland, Woosley, Guan, Schmitt, Thomas (bib0028) 2009 Abdollahzadeh, Belevich, Jokitalo, Tohka, Sierra (bib0002) 2019; 9 Alexander, Dyrby, Nilsson, Zhang (bib0005) 2019; 32 Leenen, Meek, Nieuwenhuys (bib0026) 1982; 246 Assaf, Blumenfeld-Katzir, Yovel, Basser (bib0008) 2008; 59 Drakesmith, Harms, Rudrapatna, Parker, Evans, Jones (bib0015) 2019; 203 Mordhorst, Morozova, Papazoglou, Fricke, Oeschger, Rusch, Jäger, Morawski, Weiskopf, Mohammadi (bib0029) 2021 Aboitiz, Scheibel, Fisher, Zaidel (bib0003) 1992; 598 Alexander, Hubbard, Hall, Moore, Ptito, Parker, Dyrby (bib0006) 2010; 52 Berman, Triki, Blaschko (bib0009) 2018 Halir, Flusser (bib0018) 1998; (Vol. 98 Akiba, Sano, Yanase, Ohta, Koyama (bib0004) 2019 Drobnjak, Zhang, Ianuş, Kaden, Alexander (bib0016) 2016; 75 Innocenti, Caminiti, Aboitiz (bib0020) 2015; 220 Stikov, Campbell, Stroh, Lavelée, Frey, Novek, Nuara, Ho, Bedell, Dougherty, Leppert, Boudreau, Narayanan, Duval, Cohen-Adad, Picard, Gasecka, Côté, Pike (bib0036) 2015; 118 Xu, Li, Harkins, Jiang, Xie, Kang, Does, Gore (bib0044) 2014; 103 Biedenbach, DeVito, Brown (bib0010) 1986; 61 Sepehrband, Alexander, Kurniawan, Reutens, Yang (bib0035) 2016; 29 The GIMP Development Team,. GIMP - The GNU Image Manipulation Program. URL Ronneberger, Fischer, Brox (bib0032) 2015 Schmidt, Knösche (bib0034) 2019; 15 Yakubovskiy, P., 2020. Segmentation_models.pytorch. URL van der Walt, Schönberger, Nunez-Iglesias, Boulogne, Warner, Yager, Gouillart, Yu (bib0040) 2014; 2 Byfield, P., 2021. StainTools. . Falcon, W. A., et al., 2019. PyTorch Lightning. URL Liewald, Miller, Logothetis, Wagner, Schüz (bib0027) 2014; 108 Karimi, Dou, Warfield, Gholipour (bib0023) 2020; 65 Weiskopf, Edwards, Helms, Mohammadi, Kirilina (bib0042) 2021; 3 Roy, Navab, Wachinger (bib0033) 2018 Abdollahzadeh, Belevich, Jokitalo, Sierra, Tohka (bib0001) 2021; 4 Burcaw, Fieremans, Novikov (bib0011) 2015; 114 Horowitz, Barazany, Tavor, Bernstein, Yovel, Assaf (bib0019) 2015; 220 Graf von Keyserlingk, Schramm (bib0024) 1984; 157 Caminiti, Ghaziri, Galuske, Hof, Innocenti (bib0013) 2009; 106 Paszke, Gross, Massa, Lerer, Bradbury, Chanan, Killeen, Lin, Gimelshein, Antiga, Desmaison, Kopf, Yang, DeVito, Raison, Tejani, Chilamkurthy, Steiner, Fang, Bai, Chintala (bib0031) 2019; 32 Jung, A. B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F. c.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al., 2021. Imgaug. URL West, Kelm, Carson, Does (bib0043) 2016; 125 Deng, Dong, Socher, Li, Li, Fei-Fei (bib0014) 2009 Waxman (bib0041) 1980; 3 Veraart, Nunes, Rudrapatna, Fieremans, Jones, Novikov, Shemesh (bib0039) 2020; 9 Andersson (10.1016/j.neuroimage.2022.118906_bib0007) 2020; 117 Horowitz (10.1016/j.neuroimage.2022.118906_bib0019) 2015; 220 10.1016/j.neuroimage.2022.118906_bib0012 Drakesmith (10.1016/j.neuroimage.2022.118906_bib0015) 2019; 203 10.1016/j.neuroimage.2022.118906_bib0017 10.1016/j.neuroimage.2022.118906_bib0038 Xu (10.1016/j.neuroimage.2022.118906_bib0044) 2014; 103 Abdollahzadeh (10.1016/j.neuroimage.2022.118906_bib0001) 2021; 4 Assaf (10.1016/j.neuroimage.2022.118906_bib0008) 2008; 59 Kakkar (10.1016/j.neuroimage.2022.118906_bib0022) 2018; 182 Nilsson (10.1016/j.neuroimage.2022.118906_bib0030) 2017; 30 Paszke (10.1016/j.neuroimage.2022.118906_bib0031) 2019; 32 Abdollahzadeh (10.1016/j.neuroimage.2022.118906_bib0002) 2019; 9 Akiba (10.1016/j.neuroimage.2022.118906_bib0004) 2019 Innocenti (10.1016/j.neuroimage.2022.118906_bib0020) 2015; 220 Sepehrband (10.1016/j.neuroimage.2022.118906_bib0035) 2016; 29 Stikov (10.1016/j.neuroimage.2022.118906_bib0036) 2015; 118 Drobnjak (10.1016/j.neuroimage.2022.118906_bib0016) 2016; 75 Schmidt (10.1016/j.neuroimage.2022.118906_bib0034) 2019; 15 Yushkevich (10.1016/j.neuroimage.2022.118906_bib0046) 2006; 31 Caminiti (10.1016/j.neuroimage.2022.118906_bib0013) 2009; 106 Tan (10.1016/j.neuroimage.2022.118906_sbref0037) 2019 Alexander (10.1016/j.neuroimage.2022.118906_bib0006) 2010; 52 Halir (10.1016/j.neuroimage.2022.118906_bib0018) 1998; (Vol. 98 Veraart (10.1016/j.neuroimage.2022.118906_bib0039) 2020; 9 Deng (10.1016/j.neuroimage.2022.118906_bib0014) 2009 Berman (10.1016/j.neuroimage.2022.118906_bib0009) 2018 Graf von Keyserlingk (10.1016/j.neuroimage.2022.118906_bib0024) 1984; 157 Ronneberger (10.1016/j.neuroimage.2022.118906_bib0032) 2015 Waxman (10.1016/j.neuroimage.2022.118906_bib0041) 1980; 3 10.1016/j.neuroimage.2022.118906_bib0021 Karimi (10.1016/j.neuroimage.2022.118906_bib0023) 2020; 65 Macenko (10.1016/j.neuroimage.2022.118906_bib0028) 2009 10.1016/j.neuroimage.2022.118906_bib0045 Roy (10.1016/j.neuroimage.2022.118906_bib0033) 2018 van der Walt (10.1016/j.neuroimage.2022.118906_bib0040) 2014; 2 Biedenbach (10.1016/j.neuroimage.2022.118906_bib0010) 1986; 61 Mordhorst (10.1016/j.neuroimage.2022.118906_bib0029) 2021 Leenen (10.1016/j.neuroimage.2022.118906_bib0026) 1982; 246 Alexander (10.1016/j.neuroimage.2022.118906_bib0005) 2019; 32 Aboitiz (10.1016/j.neuroimage.2022.118906_bib0003) 1992; 598 Zaimi (10.1016/j.neuroimage.2022.118906_bib0047) 2018; 8 Weiskopf (10.1016/j.neuroimage.2022.118906_bib0042) 2021; 3 Liewald (10.1016/j.neuroimage.2022.118906_bib0027) 2014; 108 Burcaw (10.1016/j.neuroimage.2022.118906_bib0011) 2015; 114 West (10.1016/j.neuroimage.2022.118906_bib0043) 2016; 125 Lee (10.1016/j.neuroimage.2022.118906_bib0025) 2019; 224 |
| References_xml | – volume: 220 start-page: 1777 year: 2015 end-page: 1788 ident: bib0019 article-title: In vivo correlation between axon diameter and conduction velocity in the human brain publication-title: Brain Struct Funct – volume: 246 start-page: 297 year: 1982 end-page: 301 ident: bib0026 article-title: Unmyelinated fibers in the pyramidal tract of the rat: A new view publication-title: Brain Research – volume: 108 start-page: 541 year: 2014 end-page: 557 ident: bib0027 article-title: Distribution of axon diameters in cortical white matter: An electron-microscopic study on three human brains and a macaque publication-title: Biol Cybern – start-page: 421 year: 2018 end-page: 429 ident: bib0033 article-title: Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks publication-title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 – volume: 114 start-page: 18 year: 2015 end-page: 37 ident: bib0011 article-title: Mesoscopic structure of neuronal tracts from time-dependent diffusion publication-title: NeuroImage – volume: 117 start-page: 33649 year: 2020 end-page: 33659 ident: bib0007 article-title: Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure–function relationship publication-title: Proc Natl Acad Sci USA – start-page: 180 year: 2021 end-page: 185 ident: bib0029 article-title: Human Axon Radii Estimation at MRI Scale publication-title: Proceedings of the 2021 German Workshop on Medical Image Computing – volume: 75 start-page: 688 year: 2016 end-page: 700 ident: bib0016 article-title: PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study publication-title: Magn Reson Med – reference: The GIMP Development Team,. GIMP - The GNU Image Manipulation Program. URL – volume: 9 start-page: 6084 year: 2019 ident: bib0002 article-title: Automated 3D Axonal Morphometry of White Matter publication-title: Sci Rep – volume: 59 start-page: 1347 year: 2008 end-page: 1354 ident: bib0008 article-title: AxCaliber: A Method for Measuring Axon Diameter Distribution from Diffusion MRI publication-title: Magn Reson Med – volume: 103 start-page: 10 year: 2014 end-page: 19 ident: bib0044 article-title: Mapping mean axon diameter and axonal volume fraction by MRI using temporal diffusion spectroscopy publication-title: Neuroimage – volume: 224 start-page: 1469 year: 2019 end-page: 1488 ident: bib0025 article-title: Along-axon diameter variation and axonal orientation dispersion revealed with 3D electron microscopy: Implications for quantifying brain white matter microstructure with histology and diffusion MRI publication-title: Brain Struct Funct – volume: 30 start-page: e3711 year: 2017 ident: bib0030 article-title: Resolution limit of cylinder diameter estimation by diffusion MRI: The impact of gradient waveform and orientation dispersion publication-title: NMR in Biomedicine – reference: Falcon, W. A., et al., 2019. PyTorch Lightning. URL – volume: 31 start-page: 1116 year: 2006 end-page: 1128 ident: bib0046 article-title: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability publication-title: NeuroImage – start-page: 234 year: 2015 end-page: 241 ident: bib0032 article-title: U-Net: Convolutional Networks for Biomedical Image Segmentation publication-title: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 – start-page: 248 year: 2009 end-page: 255 ident: bib0014 article-title: ImageNet: A large-scale hierarchical image database publication-title: 2009 IEEE Conference on Computer Vision and Pattern Recognition – volume: 32 start-page: e3841 year: 2019 ident: bib0005 article-title: Imaging brain microstructure with diffusion MRI: Practicality and applications publication-title: NMR Biomed – volume: 65 start-page: 101759 year: 2020 ident: bib0023 article-title: Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis publication-title: Medical Image Analysis – volume: 3 start-page: 141 year: 1980 end-page: 150 ident: bib0041 article-title: Determinants of conduction velocity in myelinated nerve fibers publication-title: Muscle & Nerve – volume: 4 start-page: 1 year: 2021 end-page: 14 ident: bib0001 article-title: DeepACSON automated segmentation of white matter in 3D electron microscopy publication-title: Communications Biology – volume: 9 start-page: e49855 year: 2020 ident: bib0039 article-title: Noninvasive quantification of axon radii using diffusion MRI publication-title: eLife – volume: 52 start-page: 1374 year: 2010 end-page: 1389 ident: bib0006 article-title: Orientationally invariant indices of axon diameter and density from diffusion MRI publication-title: NeuroImage – start-page: 6105 year: 2019 end-page: 6114 ident: bib0037 article-title: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks publication-title: Proceedings of the 36th International Conference on Machine Learning – volume: 29 start-page: 293 year: 2016 end-page: 308 ident: bib0035 article-title: Towards higher sensitivity and stability of axon diameter estimation with diffusion-weighted MRI publication-title: NMR Biomed – volume: 3 start-page: 570 year: 2021 end-page: 588 ident: bib0042 article-title: Quantitative magnetic resonance imaging of brain anatomy and in vivo histology publication-title: Nat Rev Phys – volume: 598 start-page: 143 year: 1992 end-page: 153 ident: bib0003 article-title: Fiber composition of the human corpus callosum publication-title: Brain Research – volume: 125 start-page: 1155 year: 2016 end-page: 1158 ident: bib0043 article-title: A revised model for estimating g-ratio from MRI publication-title: Neuroimage – volume: 106 start-page: 19551 year: 2009 end-page: 19556 ident: bib0013 article-title: Evolution amplified processing with temporally dispersed slow neuronal connectivity in primates publication-title: Proc. Natl. Acad. Sci. U.S.A. – volume: 118 start-page: 397 year: 2015 end-page: 405 ident: bib0036 article-title: In vivo histology of the myelin g-ratio with magnetic resonance imaging publication-title: Neuroimage – reference: Jung, A. B., Wada, K., Crall, J., Tanaka, S., Graving, J., Reinders, C., Yadav, S., Banerjee, J., Vecsei, G., Kraft, A., Rui, Z., Borovec, J., Vallentin, C., Zhydenko, S., Pfeiffer, K., Cook, B., Fernández, I., De Rainville, F. c.-M., Weng, C.-H., Ayala-Acevedo, A., Meudec, R., Laporte, M., et al., 2021. Imgaug. URL – volume: (Vol. 98, start-page: 125 year: 1998 end-page: 132 ident: bib0018 publication-title: Numerically stable direct least squares fitting of ellipses – volume: 8 start-page: 3816 year: 2018 ident: bib0047 article-title: AxonDeepSeg: Automatic axon and myelin segmentation from microscopy data using convolutional neural networks publication-title: Sci Rep – reference: . – start-page: 2623 year: 2019 end-page: 2631 ident: bib0004 article-title: Optuna: A Next-generation Hyperparameter Optimization Framework publication-title: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – volume: 157 start-page: 97 year: 1984 end-page: 111 ident: bib0024 article-title: Diameter of axons and thickness of myelin sheaths of the pyramidal tract fibres in the adult human medullary pyramid publication-title: Anat Anz – volume: 61 start-page: 303 year: 1986 end-page: 310 ident: bib0010 article-title: Pyramidal tract of the cat: Axon size and morphology publication-title: Exp Brain Res – volume: 220 start-page: 1789 year: 2015 end-page: 1790 ident: bib0020 article-title: Comments on the paper by Horowitz et al. (2014) publication-title: Brain Struct Funct – volume: 203 start-page: 116186 year: 2019 ident: bib0015 article-title: Estimating axon conduction velocity in vivo from microstructural MRI publication-title: NeuroImage – volume: 15 start-page: e1007004 year: 2019 ident: bib0034 article-title: Action potential propagation and synchronisation in myelinated axons publication-title: PLOS Computational Biology – reference: Byfield, P., 2021. StainTools. – start-page: 4413 year: 2018 end-page: 4421 ident: bib0009 article-title: The Lovász-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 182 start-page: 314 year: 2018 end-page: 328 ident: bib0022 article-title: Low frequency oscillating gradient spin-echo sequences improve sensitivity to axon diameter: An experimental study in viable nerve tissue publication-title: Neuroimage – start-page: 1107 year: 2009 end-page: 1110 ident: bib0028 article-title: A method for normalizing histology slides for quantitative analysis publication-title: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro – volume: 32 year: 2019 ident: bib0031 article-title: PyTorch: An Imperative Style, High-Performance Deep Learning Library publication-title: Advances in Neural Information Processing Systems – volume: 2 start-page: e453 year: 2014 ident: bib0040 article-title: Scikit-image: Image processing in Python publication-title: PeerJ – reference: Yakubovskiy, P., 2020. Segmentation_models.pytorch. URL – volume: 61 start-page: 303 issue: 2 year: 1986 ident: 10.1016/j.neuroimage.2022.118906_bib0010 article-title: Pyramidal tract of the cat: Axon size and morphology publication-title: Exp Brain Res doi: 10.1007/BF00239520 – volume: 246 start-page: 297 issue: 2 year: 1982 ident: 10.1016/j.neuroimage.2022.118906_bib0026 article-title: Unmyelinated fibers in the pyramidal tract of the rat: A new view publication-title: Brain Research doi: 10.1016/0006-8993(82)91179-9 – volume: 4 start-page: 1 issue: 1 year: 2021 ident: 10.1016/j.neuroimage.2022.118906_bib0001 article-title: DeepACSON automated segmentation of white matter in 3D electron microscopy publication-title: Communications Biology doi: 10.1038/s42003-021-01699-w – volume: 108 start-page: 541 issue: 5 year: 2014 ident: 10.1016/j.neuroimage.2022.118906_bib0027 article-title: Distribution of axon diameters in cortical white matter: An electron-microscopic study on three human brains and a macaque publication-title: Biol Cybern doi: 10.1007/s00422-014-0626-2 – volume: 103 start-page: 10 year: 2014 ident: 10.1016/j.neuroimage.2022.118906_bib0044 article-title: Mapping mean axon diameter and axonal volume fraction by MRI using temporal diffusion spectroscopy publication-title: Neuroimage doi: 10.1016/j.neuroimage.2014.09.006 – volume: 203 start-page: 116186 year: 2019 ident: 10.1016/j.neuroimage.2022.118906_bib0015 article-title: Estimating axon conduction velocity in vivo from microstructural MRI publication-title: NeuroImage doi: 10.1016/j.neuroimage.2019.116186 – volume: 75 start-page: 688 issue: 2 year: 2016 ident: 10.1016/j.neuroimage.2022.118906_bib0016 article-title: PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study publication-title: Magn Reson Med doi: 10.1002/mrm.25631 – volume: 65 start-page: 101759 year: 2020 ident: 10.1016/j.neuroimage.2022.118906_bib0023 article-title: Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis publication-title: Medical Image Analysis doi: 10.1016/j.media.2020.101759 – volume: 30 start-page: e3711 issue: 7 year: 2017 ident: 10.1016/j.neuroimage.2022.118906_bib0030 article-title: Resolution limit of cylinder diameter estimation by diffusion MRI: The impact of gradient waveform and orientation dispersion publication-title: NMR in Biomedicine doi: 10.1002/nbm.3711 – volume: 15 start-page: e1007004 issue: 10 year: 2019 ident: 10.1016/j.neuroimage.2022.118906_bib0034 article-title: Action potential propagation and synchronisation in myelinated axons publication-title: PLOS Computational Biology doi: 10.1371/journal.pcbi.1007004 – volume: 31 start-page: 1116 issue: 3 year: 2006 ident: 10.1016/j.neuroimage.2022.118906_bib0046 article-title: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability publication-title: NeuroImage doi: 10.1016/j.neuroimage.2006.01.015 – ident: 10.1016/j.neuroimage.2022.118906_bib0045 – volume: 224 start-page: 1469 issue: 4 year: 2019 ident: 10.1016/j.neuroimage.2022.118906_bib0025 article-title: Along-axon diameter variation and axonal orientation dispersion revealed with 3D electron microscopy: Implications for quantifying brain white matter microstructure with histology and diffusion MRI publication-title: Brain Struct Funct doi: 10.1007/s00429-019-01844-6 – volume: 598 start-page: 143 issue: 1 year: 1992 ident: 10.1016/j.neuroimage.2022.118906_bib0003 article-title: Fiber composition of the human corpus callosum publication-title: Brain Research doi: 10.1016/0006-8993(92)90178-C – volume: 220 start-page: 1789 issue: 3 year: 2015 ident: 10.1016/j.neuroimage.2022.118906_bib0020 article-title: Comments on the paper by Horowitz et al. (2014) publication-title: Brain Struct Funct doi: 10.1007/s00429-014-0974-7 – volume: 157 start-page: 97 issue: 2 year: 1984 ident: 10.1016/j.neuroimage.2022.118906_bib0024 article-title: Diameter of axons and thickness of myelin sheaths of the pyramidal tract fibres in the adult human medullary pyramid publication-title: Anat Anz – volume: 220 start-page: 1777 issue: 3 year: 2015 ident: 10.1016/j.neuroimage.2022.118906_bib0019 article-title: In vivo correlation between axon diameter and conduction velocity in the human brain publication-title: Brain Struct Funct doi: 10.1007/s00429-014-0871-0 – volume: 114 start-page: 18 year: 2015 ident: 10.1016/j.neuroimage.2022.118906_bib0011 article-title: Mesoscopic structure of neuronal tracts from time-dependent diffusion publication-title: NeuroImage doi: 10.1016/j.neuroimage.2015.03.061 – volume: 32 year: 2019 ident: 10.1016/j.neuroimage.2022.118906_bib0031 article-title: PyTorch: An Imperative Style, High-Performance Deep Learning Library publication-title: Advances in Neural Information Processing Systems – volume: 117 start-page: 33649 issue: 52 year: 2020 ident: 10.1016/j.neuroimage.2022.118906_bib0007 article-title: Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure–function relationship publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.2012533117 – volume: 9 start-page: e49855 year: 2020 ident: 10.1016/j.neuroimage.2022.118906_bib0039 article-title: Noninvasive quantification of axon radii using diffusion MRI publication-title: eLife doi: 10.7554/eLife.49855 – volume: 32 start-page: e3841 issue: 4 year: 2019 ident: 10.1016/j.neuroimage.2022.118906_bib0005 article-title: Imaging brain microstructure with diffusion MRI: Practicality and applications publication-title: NMR Biomed doi: 10.1002/nbm.3841 – volume: 3 start-page: 141 issue: 2 year: 1980 ident: 10.1016/j.neuroimage.2022.118906_bib0041 article-title: Determinants of conduction velocity in myelinated nerve fibers publication-title: Muscle & Nerve doi: 10.1002/mus.880030207 – start-page: 180 year: 2021 ident: 10.1016/j.neuroimage.2022.118906_bib0029 article-title: Human Axon Radii Estimation at MRI Scale – ident: 10.1016/j.neuroimage.2022.118906_bib0017 – ident: 10.1016/j.neuroimage.2022.118906_bib0038 – volume: 9 start-page: 6084 issue: 1 year: 2019 ident: 10.1016/j.neuroimage.2022.118906_bib0002 article-title: Automated 3D Axonal Morphometry of White Matter publication-title: Sci Rep doi: 10.1038/s41598-019-42648-2 – ident: 10.1016/j.neuroimage.2022.118906_bib0021 – volume: 59 start-page: 1347 issue: 6 year: 2008 ident: 10.1016/j.neuroimage.2022.118906_bib0008 article-title: AxCaliber: A Method for Measuring Axon Diameter Distribution from Diffusion MRI publication-title: Magn Reson Med doi: 10.1002/mrm.21577 – volume: 182 start-page: 314 year: 2018 ident: 10.1016/j.neuroimage.2022.118906_bib0022 article-title: Low frequency oscillating gradient spin-echo sequences improve sensitivity to axon diameter: An experimental study in viable nerve tissue publication-title: Neuroimage doi: 10.1016/j.neuroimage.2017.07.060 – start-page: 6105 year: 2019 ident: 10.1016/j.neuroimage.2022.118906_sbref0037 article-title: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks – start-page: 248 year: 2009 ident: 10.1016/j.neuroimage.2022.118906_bib0014 article-title: ImageNet: A large-scale hierarchical image database – start-page: 421 year: 2018 ident: 10.1016/j.neuroimage.2022.118906_bib0033 article-title: Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks – volume: 8 start-page: 3816 issue: 1 year: 2018 ident: 10.1016/j.neuroimage.2022.118906_bib0047 article-title: AxonDeepSeg: Automatic axon and myelin segmentation from microscopy data using convolutional neural networks publication-title: Sci Rep doi: 10.1038/s41598-018-22181-4 – start-page: 2623 year: 2019 ident: 10.1016/j.neuroimage.2022.118906_bib0004 article-title: Optuna: A Next-generation Hyperparameter Optimization Framework – volume: 52 start-page: 1374 issue: 4 year: 2010 ident: 10.1016/j.neuroimage.2022.118906_bib0006 article-title: Orientationally invariant indices of axon diameter and density from diffusion MRI publication-title: NeuroImage doi: 10.1016/j.neuroimage.2010.05.043 – start-page: 234 year: 2015 ident: 10.1016/j.neuroimage.2022.118906_bib0032 article-title: U-Net: Convolutional Networks for Biomedical Image Segmentation – volume: 125 start-page: 1155 year: 2016 ident: 10.1016/j.neuroimage.2022.118906_bib0043 article-title: A revised model for estimating g-ratio from MRI publication-title: Neuroimage doi: 10.1016/j.neuroimage.2015.08.017 – start-page: 1107 year: 2009 ident: 10.1016/j.neuroimage.2022.118906_bib0028 article-title: A method for normalizing histology slides for quantitative analysis – volume: 106 start-page: 19551 issue: 46 year: 2009 ident: 10.1016/j.neuroimage.2022.118906_bib0013 article-title: Evolution amplified processing with temporally dispersed slow neuronal connectivity in primates publication-title: Proc. Natl. Acad. Sci. U.S.A. doi: 10.1073/pnas.0907655106 – volume: (Vol. 98, start-page: 125 year: 1998 ident: 10.1016/j.neuroimage.2022.118906_bib0018 publication-title: Numerically stable direct least squares fitting of ellipses – start-page: 4413 year: 2018 ident: 10.1016/j.neuroimage.2022.118906_bib0009 article-title: The Lovász-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks – volume: 2 start-page: e453 year: 2014 ident: 10.1016/j.neuroimage.2022.118906_bib0040 article-title: Scikit-image: Image processing in Python publication-title: PeerJ doi: 10.7717/peerj.453 – ident: 10.1016/j.neuroimage.2022.118906_bib0012 – volume: 118 start-page: 397 year: 2015 ident: 10.1016/j.neuroimage.2022.118906_bib0036 article-title: In vivo histology of the myelin g-ratio with magnetic resonance imaging publication-title: Neuroimage doi: 10.1016/j.neuroimage.2015.05.023 – volume: 3 start-page: 570 issue: 8 year: 2021 ident: 10.1016/j.neuroimage.2022.118906_bib0042 article-title: Quantitative magnetic resonance imaging of brain anatomy and in vivo histology publication-title: Nat Rev Phys doi: 10.1038/s42254-021-00326-1 – volume: 29 start-page: 293 issue: 3 year: 2016 ident: 10.1016/j.neuroimage.2022.118906_bib0035 article-title: Towards higher sensitivity and stability of axon diameter estimation with diffusion-weighted MRI publication-title: NMR Biomed doi: 10.1002/nbm.3462 |
| SSID | ssj0009148 |
| Score | 2.4174454 |
| Snippet | •A pipeline for automated estimation of ensemble mean axon radii at MRI voxel scale•Incorporation of 4 human white matter brain tissue samples for training and... Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal... |
| SourceID | doaj proquest pubmed crossref elsevier |
| SourceType | Open Website Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 118906 |
| SubjectTerms | Aged Aged, 80 and over Anatomy Axon radii distribution Axons Axons - ultrastructure Brain Brain architecture Corpus callosum Cross microscopy Deep Learning Estimates Female Histology Humans Light microscopy Magnetic Resonance Imaging Male Microscopy Microscopy - methods Middle Aged MRI-based axon radius Nervous system Neuroanatomy Neuroimaging - methods Segmentation Variation White Matter - diagnostic imaging White Matter - ultrastructure |
| SummonAdditionalLinks | – databaseName: Health & Medical Collection dbid: 7X7 link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagIMSF8ia0ICNxtYgTx3bEAVFEBRKtECpob5btONWiNin7qOi_Z8ZxsnAArcQ1m4mSnfE3M57xN4S8rJzioZUlQ3Y1JsAqmOO2YsGWFhJoKwa6pm-f1PGxns3qz2nDbZnaKkdMjEDd9B73yF8VslAch0-LNxc_GE6NwupqGqFxndzAsdlo52qmNqS7XAxH4aqSac7r1Mkz9HdFvsj5OaxayBKLArBD1zj36Df3FFn8__BSf4tCozc63P3f77hL7qQ4lL4dDOceuRa6--TWUaq0PyDNSeynXVJLI-9lOqN0Geg0mIRCvEuPvnxk6AkbGqf9Ufuz7-jCNvM1iE6snxTb60_pGe4E0HPsAcTTMFcPydfD9yfvPrA0kYF5COtWDIBd1a4MVufSYgm24N7yVkEMaHkpHW-8bIRsgpSAE161wqOTbCDOUMFpVz4iO13fhSeE2qoNpS891y6HFLHQLRZwvXataL2uVEbUqAjjE105Ts04M2Nf2nezUaFBFZpBhRnhk-TFQNmxhcwB6nq6H0m344V-cWrSGjZKCC9zW1SNFyJwVztXBQRAiJkgkc4zUo-WYsZzrYDE8KD5Fi_wepJNsc8Q02wpvT8am0kYtDQbS8vIi-lnQA8sCdku9Ot4D-YMgMMZeTwY9PQflFUOeq3qp_9--B65jW8ydDPtk53VYh2ekZv-cjVfLp7HBfkLdP49pg priority: 102 providerName: ProQuest |
| Title | Towards a representative reference for MRI-based human axon radius assessment using light microscopy |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1053811922000362 https://dx.doi.org/10.1016/j.neuroimage.2022.118906 https://www.ncbi.nlm.nih.gov/pubmed/35032659 https://www.proquest.com/docview/2627122294 https://www.proquest.com/docview/2620080319 https://doaj.org/article/744c60a25dc44e1b9bb5e48801297210 |
| Volume | 249 |
| WOSCitedRecordID | wos000766269800008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1095-9572 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: AIEXJ dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1095-9572 dateEnd: 20251014 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M7P dateStart: 19980501 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1095-9572 dateEnd: 20251014 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: 7X7 dateStart: 20020801 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1095-9572 dateEnd: 20251014 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: BENPR dateStart: 19980501 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Psychology Database customDbUrl: eissn: 1095-9572 dateEnd: 20251014 omitProxy: false ssIdentifier: ssj0009148 issn: 1053-8119 databaseCode: M2M dateStart: 20020801 isFulltext: true titleUrlDefault: https://www.proquest.com/psychology providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB5BQagXxJtAWRmJa0ScOHEiThS1AoldraqC9mbZjoMWtVm0j6r8e2ZsJ5QDYg9cfEgykeUZz3wjj78BeFMayV1XFSmxq6UCrSI1XJep04XGBFqLQNf09bOczerFopnfaPVFNWGBHjgs3FsphK0ynZetFcJx0xhTOrI6DFSYvfhsPZPNkEwNdLuI8mPdTqjm8uyQy0vco5gT5jl6irqhLkc3gpHn7P8jJv0Nc_rYc_oA7kfQyN6HyT6EW65_BPem8Vj8MbTnvvh1wzTzJJXxQtGVY2MXEYbglE3PPqUUtlrmW_Mxfb3q2Vq3yx2KjhSdjGrhv7ELStvZJRXs0dWVn0_gy-nJ-YePaWyfkFrEYNsUvbBsTOF0nVWazktzbjXvJAI2zYvK8NZWrahaV1W4qa3shKWI1iIokM7UpngKB_2qd8-B6bJzhS0sr02G-Vxed3TaamvTic7WpUxADuuobOQWpxYXF2ooIvuufmtAkQZU0EACfJT8Efg19pA5JlWN3xNDtn-AdqOi3ah_2U0CzaBoNVxCRbeJP1ruMYF3o2wEKgGA7Cl9NNiVig5jo_Iql5x6q4sEXo-vcavT-Y3u3WrnvyGAj04zgWfBHsc1KMoM9Vo2L_7H2ryEQ5pvKFA6goPteudewV17tV1u1hO4LRfSj_UE7hyfzOZnE7_7cJzmUxrl_BcV1TIP |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgoALb8pCASPBMWLtOHEihBCvqqtuVggtqDfXdpxqq3ZT9lHon-I3MpPXwgG0lx64bjJWNv78zTiemQ_geWQV90UcBtRdLZCIisByEwXehAY30EbW7Zq-DtVolOzvp5824GdbC0NplS0nVkSdl46-kb8UsVCcxKflm9NvAalG0elqK6FRw2LPn3_HLdv89eADzu8LIXY-jt_vBo2qQOAwNFkESE4qtaE3ST82dIwouDO8UBjHGB7GlucuzmWc-zhGrDtVSEdEn6OvVN4mNsRxL8FliZ6QFBMyka2a_HJZl95FYZBwnjaZQ3U-WdWfcnKCLIG7UiGQq5KUdJZ-c4eVasAfXvFvUW_l_XZu_m_v7RbcaOJs9rZeGLdhw0_vwNWsySS4C_m4yheeM8Oqvp5NDdaZZ53wCsN4nmWfBwF5-pxVaobM_CinbGbyyRJNu66mjMoHDtkxfelgJ5TjSNU-5_fgy4X8x_uwOS2n_gEwExU-dKHjie3jFlgkBR1Qu8QWsnBJpHqg2onXrmnHTqogx7rNuzvSK8hogoyuIdMD3lme1i1J1rB5R9jq7qem4tUP5exQNxyllZQu7hsR5U5Kz21qbeSJ4DEmVIL3e5C2yNRt3S56GhxossYDvOpsm9iujtnWtN5uwa0bjp3rFbJ78Ky7jOxIR15m6stldQ_tidDP9GCrXkDdOwijPs5rlD789-BP4druOBvq4WC09wiu01PVmVvbsLmYLf1juOLOFpP57ElFBgwOLnoV_QIUI5mK |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLaq48H4sFDASHKPGiRMnQghR2hWrtqtVVVBvru041aJ2t-yj0L_Gr2MmcbJwAO2lB65JxnLiz9_MxPMAeJ0YyV2ZxgFVVwsEoiIwXCeB07FGB1qLulzTl305GGTHx_lwDX42uTAUVtlwYkXUxcTSP_KtKI0kp-bTYqv0YRHDnd77i28BdZCik9amnUYNkT139R3dt9m7_g6u9Zso6u0effwU-A4DgUUzZR4gUcncxE5nYarpSDHiVvNSok2jeZwaXti0EGnh0hRxb2UpLJF-gXpTOpOZGMe9AesyRqenA-vbu4Ph4bLkLxd1Il4SBxnnuY8jqqPLqmqVo3PkDPRRowiZK8up69JvyrHqIfCHjvybDVzpwt6d__kr3oXb3gJnH-otcw_W3Pg-bBz4GIMHUBxVkcQzpllV8dNnZ1061rZkYWjps4PDfkA2QMGqPodM_5iM2VQXowWKtvVOGSUWnLIz-gfCzin6kfKArh7C52t5x0fQGU_G7gkwnZQutrHlmQnROY6yko6ubWZKUdoskV2QDQiU9YXaqV_ImWoi8r6qJXwUwUfV8OkCbyUv6mIlK8hsE87a56nceHVhMj1Vnr2UFMKmoY6SwgrhuMmNSRxRP1qLMuJhF_IGparJ6EUdhAONVpjA21bWW321Nbei9GYDdOXZd6aWKO_Cq_Y28iYdhumxmyyqZ8hbQg3Uhcf1Zmq_QZyEuK5J_vTfg7-EDdw8ar8_2HsGt2hSdUjXJnTm04V7Djft5Xw0m77wzMDg5Lq30S-HZaOt |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Towards+a+representative+reference+for+MRI-based+human+axon+radius+assessment+using+light+microscopy&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Mordhorst%2C+Laurin&rft.au=Morozova%2C+Maria&rft.au=Papazoglou%2C+Sebastian&rft.au=Fricke%2C+Bj%C3%B6rn&rft.date=2022-04-01&rft.issn=1095-9572&rft.eissn=1095-9572&rft.volume=249&rft.spage=118906&rft_id=info:doi/10.1016%2Fj.neuroimage.2022.118906&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-8119&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-8119&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-8119&client=summon |