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Automated interhemispheric surface extraction in T1-weighted MRI using intensity and symmetry information
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
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2014 (English)In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 222, 97-105 p.Article in journal (Refereed) Published
Abstract [en]

Background: Localizing the human interhemispheric region is of interest in image analysis mainly because it can be used for hemisphere separation and as a preprocessing step for interhemispheric structure localization. Many existing methods focus on only one of these applications. New method: Here a new Intensity and Symmetry based Interhemispheric Surface extraction method (ISIS) that enables both applications is presented. A combination of voxel intensity and local symmetry is used to optimize a surface from T1-weighted MRI. Results: ISIS was evaluated in regard to cerebral hemisphere separation using manual segmentations. It was also evaluated in regard to being a preprocessing step for interhemispheric structure localization using manually placed landmarks. Comparison with existing methods: Results were compared to cerebral hemisphere separations by Brain-Visa and Freesurfer as well as to a midsagittal plane (MSP) extraction method. ISIS had less misclassified voxels than BrainVisa (ISIS: 0.119+/-0.114%, BrainVisa: 0.138+/-0.084%, p=0.020). Freesurfer had less misclassified voxels than ISIS for one dataset (ISIS: 0.063+/-0.056%, Freesurfer: 0.049+/-0.044%, p=0.019), but failed to produce usable results for another. Total voxel distance from all manual landmarks did not differ significantly between ISIS and the MSP method (ISIS: 4.00+/-1.88, MSP: 4.47+/-4.97). Conclusions: ISIS was found successful in both cerebral hemisphere separation and as a preprocessing step for interhemispheric structure localization. It needs no time consuming preprocessing and extracts the interhemispheric surface in less than 30 s.

Place, publisher, year, edition, pages
2014. Vol. 222, 97-105 p.
Keyword [en]
Interhemispheric surface, Graph cut, Hemisphere separation, Midsagittal plane, Anterior commissure, Posterior commissure
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-221919DOI: 10.1016/j.jneumeth.2013.11.007ISI: 000331672000012OAI: oai:DiVA.org:uu-221919DiVA: diva2:710455
Available from: 2014-04-07 Created: 2014-04-07 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Analysis of Human Brain MRI: Contributions to Regional Volume Studies
Open this publication in new window or tab >>Analysis of Human Brain MRI: Contributions to Regional Volume Studies
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many disorders are associated with regional brain volumes. The analysis of these volumes from MR images often requires sequential processing steps such as localization and delineation. It is common to perform volumetric normalization using intracranial volume (ICV, the total volume inside the cranial cavity) when comparing regional brain volumes, since head size varies considerably between individuals. Multiple methods for estimating ICV and procedures for volume normalization exist.

A method for interhemispheric surface localization and extraction, using both intensity and symmetry information and without time consuming pre-processing, was developed. Evaluations of hemisphere division accuracy as well as suitability as a pre-processing step for interhemispheric structure localization were made. The performance of the method was comparable to that of methods focusing on either of these tasks, making it suited for use in many different studies.

Automated ICV estimations from Freesurfer and SPM were evaluated using 399 reference segmentations. Both methods overestimated ICV and estimations using Freesurfer contained errors associated with skull-size. Estimations from SPM contained errors associated with gender and atrophy. An experiment showed that the choice of method can affect study results.

Manual ICV estimation is very time consuming, but can be performed using only a subset of voxels in an image to increase speed and decrease manual labor. Segmenting every nth slice and stereology were evaluated in terms of required manual labor and estimation error, using the previously created ICV references. An illustration showing how much manual labor is required for a given estimation error using different combinations of n and stereology grid spacing was presented.

Finally, different procedures for ICV normalization of regional brain volumes when investigating gender related volume differences were theoretically explained and evaluated using both simulated and real data. Resulting volume differences were seen to depend on the procedure used. A suggested workflow for procedure selection was presented.

Methodological contributions that can aid the analysis of the human brain have been presented. The performed studies also contribute to the understanding of important methodological considerations for regional brain volume analysis.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 73 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1000
Keyword
MRI, Brain, Volume, Hemisphere, Intracranial volume, Image analysis
National Category
Medical Image Processing
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-222376 (URN)978-91-554-8957-1 (ISBN)
Public defence
2014-06-10, Gunnesalen, Entrance 10, Uppsala University Hospital, Uppsala, 09:15 (Swedish)
Opponent
Supervisors
Available from: 2014-05-20 Created: 2014-04-10 Last updated: 2014-06-30

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Nordenskjöld, RichardLarsson, Elna-MarieAhlström, HåkanJohansson, LarsKullberg, Joel

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