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Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials

PURPOSE: Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a l...

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Autores principales: Brownhill, Daniel, Chen, Yachin, Kreilkamp, Barbara A. K., de Bezenac, Christophe, Denby, Christine, Bracewell, Martyn, Biswas, Shubhabrata, Das, Kumar, Marson, Anthony G., Keller, Simon S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005416/
https://www.ncbi.nlm.nih.gov/pubmed/34661698
http://dx.doi.org/10.1007/s00234-021-02811-x
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author Brownhill, Daniel
Chen, Yachin
Kreilkamp, Barbara A. K.
de Bezenac, Christophe
Denby, Christine
Bracewell, Martyn
Biswas, Shubhabrata
Das, Kumar
Marson, Anthony G.
Keller, Simon S.
author_facet Brownhill, Daniel
Chen, Yachin
Kreilkamp, Barbara A. K.
de Bezenac, Christophe
Denby, Christine
Bracewell, Martyn
Biswas, Shubhabrata
Das, Kumar
Marson, Anthony G.
Keller, Simon S.
author_sort Brownhill, Daniel
collection PubMed
description PURPOSE: Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a lost opportunity to perform quantitative image analysis. We determine the performance of a modified subcortical segmentation technique applied to 2D images in patients with idiopathic generalised epilepsy (IGE). METHODS: Volume estimates were derived from 2D (0.4 × 0.4 × 3 mm) and 3D (1 × 1x1mm) T1-weighted acquisitions in 31 patients with IGE and 39 healthy controls. 2D image segmentation was performed using a modified FSL FIRST (FMRIB Integrated Registration and Segmentation Tool) pipeline requiring additional image reorientation, cropping, interpolation and brain extraction prior to conventional FIRST segmentation. Consistency between segmentations was assessed using Dice coefficients and volumes across both approaches were compared between patients and controls. The influence of slice thickness on consistency was further assessed using 2D images with slice thickness increased to 6 mm. RESULTS: All average Dice coefficients showed excellent agreement between 2 and 3D images across subcortical structures (0.86–0.96). Most 2D volumes were consistently slightly lower compared to 3D volumes. 2D images with increased slice thickness showed lower agreement with 3D images with lower Dice coefficients (0.55–0.83). Significant volume reduction of the left and right thalamus and putamen was observed in patients relative to controls across 2D and 3D images. CONCLUSION: Automated subcortical volume estimation of 2D images with a resolution of 0.4 × 0.4x3mm using a modified FIRST pipeline is consistent with volumes derived from 3D images, although this consistency decreases with an increased slice thickness. Thalamic and putamen atrophy has previously been reported in patients with IGE. Automated subcortical volume estimation from 2D images is feasible and most reliable at using in-plane acquisitions greater than 1 mm x 1 mm and provides an opportunity to perform quantitative image analysis studies in clinical trials. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00234-021-02811-x.
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spelling pubmed-90054162022-04-14 Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials Brownhill, Daniel Chen, Yachin Kreilkamp, Barbara A. K. de Bezenac, Christophe Denby, Christine Bracewell, Martyn Biswas, Shubhabrata Das, Kumar Marson, Anthony G. Keller, Simon S. Neuroradiology Diagnostic Neuroradiology PURPOSE: Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a lost opportunity to perform quantitative image analysis. We determine the performance of a modified subcortical segmentation technique applied to 2D images in patients with idiopathic generalised epilepsy (IGE). METHODS: Volume estimates were derived from 2D (0.4 × 0.4 × 3 mm) and 3D (1 × 1x1mm) T1-weighted acquisitions in 31 patients with IGE and 39 healthy controls. 2D image segmentation was performed using a modified FSL FIRST (FMRIB Integrated Registration and Segmentation Tool) pipeline requiring additional image reorientation, cropping, interpolation and brain extraction prior to conventional FIRST segmentation. Consistency between segmentations was assessed using Dice coefficients and volumes across both approaches were compared between patients and controls. The influence of slice thickness on consistency was further assessed using 2D images with slice thickness increased to 6 mm. RESULTS: All average Dice coefficients showed excellent agreement between 2 and 3D images across subcortical structures (0.86–0.96). Most 2D volumes were consistently slightly lower compared to 3D volumes. 2D images with increased slice thickness showed lower agreement with 3D images with lower Dice coefficients (0.55–0.83). Significant volume reduction of the left and right thalamus and putamen was observed in patients relative to controls across 2D and 3D images. CONCLUSION: Automated subcortical volume estimation of 2D images with a resolution of 0.4 × 0.4x3mm using a modified FIRST pipeline is consistent with volumes derived from 3D images, although this consistency decreases with an increased slice thickness. Thalamic and putamen atrophy has previously been reported in patients with IGE. Automated subcortical volume estimation from 2D images is feasible and most reliable at using in-plane acquisitions greater than 1 mm x 1 mm and provides an opportunity to perform quantitative image analysis studies in clinical trials. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00234-021-02811-x. Springer Berlin Heidelberg 2021-10-18 2022 /pmc/articles/PMC9005416/ /pubmed/34661698 http://dx.doi.org/10.1007/s00234-021-02811-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Diagnostic Neuroradiology
Brownhill, Daniel
Chen, Yachin
Kreilkamp, Barbara A. K.
de Bezenac, Christophe
Denby, Christine
Bracewell, Martyn
Biswas, Shubhabrata
Das, Kumar
Marson, Anthony G.
Keller, Simon S.
Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials
title Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials
title_full Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials
title_fullStr Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials
title_full_unstemmed Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials
title_short Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials
title_sort automated subcortical volume estimation from 2d mri in epilepsy and implications for clinical trials
topic Diagnostic Neuroradiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005416/
https://www.ncbi.nlm.nih.gov/pubmed/34661698
http://dx.doi.org/10.1007/s00234-021-02811-x
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