Subcortical brain segmentation of two dimensional T1-weighted data sets with FMRIB's Integrated Registration and Segmentation Tool (FIRST)
| Autor: | Ludwig Kappos, Michaela Andělová, Till Sprenger, Michael Amann, Nicole Mueller-Lenke, Kerstin Bendfeldt, Stefan Traud, Ernst-Wilhelm Radue, Christoph Stippich, Julia Reinhardt, Armanda Pfister, Stefano Magon |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Adult
Male Multiple Sclerosis Cognitive Neuroscience Caudate nucleus FMRIB's Integrated Registration and Segmentation Tool Grey matter Nucleus accumbens lcsh:Computer applications to medicine. Medical informatics lcsh:RC346-429 Young Adult Segmentation Basal ganglia Image Interpretation Computer-Assisted medicine Brain segmentation Humans Radiology Nuclear Medicine and imaging Two-dimensional data Gray Matter lcsh:Neurology. Diseases of the nervous system Aged medicine.diagnostic_test business.industry T1-weighted data Putamen Brain Magnetic resonance imaging Pattern recognition Regular Article Anatomy Middle Aged Magnetic Resonance Imaging medicine.anatomical_structure Neurology lcsh:R858-859.7 Female Neurology (clinical) Artificial intelligence Atrophy business Psychology Software |
| Zdroj: | NeuroImage : Clinical NeuroImage: Clinical, Vol 7, Iss C, Pp 43-52 (2015) |
| ISSN: | 2213-1582 |
| Popis: | Brain atrophy has been identified as an important contributing factor to the development of disability in multiple sclerosis (MS). In this respect, more and more interest is focussing on the role of deep grey matter (DGM) areas. Novel data analysis pipelines are available for the automatic segmentation of DGM using three-dimensional (3D) MRI data. However, in clinical trials, often no such high-resolution data are acquired and hence no conclusions regarding the impact of new treatments on DGM atrophy were possible so far. In this work, we used FMRIB's Integrated Registration and Segmentation Tool (FIRST) to evaluate the possibility of segmenting DGM structures using standard two-dimensional (2D) T1-weighted MRI. In a cohort of 70 MS patients, both 2D and 3D T1-weighted data were acquired. The thalamus, putamen, pallidum, nucleus accumbens, and caudate nucleus were bilaterally segmented using FIRST. Volumes were calculated for each structure and for the sum of basal ganglia (BG) as well as for the total DGM. The accuracy and reliability of the 2D data segmentation were compared with the respective results of 3D segmentations using volume difference, volume overlap and intra-class correlation coefficients (ICCs). The mean differences for the individual substructures were between 1.3% (putamen) and −25.2% (nucleus accumbens). The respective values for the BG were −2.7% and for DGM 1.3%. Mean volume overlap was between 89.1% (thalamus) and 61.5% (nucleus accumbens); BG: 84.1%; DGM: 86.3%. Regarding ICC, all structures showed good agreement with the exception of the nucleus accumbens. The results of the segmentation were additionally validated through expert manual delineation of the caudate nucleus and putamen in a subset of the 3D data. In conclusion, we demonstrate that subcortical segmentation of 2D data are feasible using FIRST. The larger subcortical GM structures can be segmented with high consistency. This forms the basis for the application of FIRST in large 2D MRI data sets of clinical trials in order to determine the impact of therapeutic interventions on DGM atrophy in MS. Highlights • Segmentation of deep grey matter (DGM) using 2-dimensional MRI data is challenging. • We performed a systematic comparison between 2 and 3D data segmentation using FIRST. • The DGM volumes of 70 multiple sclerosis patients were statistically compared. • We found a good agreement of the segmentation results for the large DGM structures. • Our results indicate that FIRST is suitable for the segmentation of 2D data sets acquired in multicentre studies. |
| Databáze: | OpenAIRE |
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