A white matter lesion-filling approach to improve brain tissue volume measurements
Multiple sclerosis white matter (WM) lesions can affect brain tissue volume measurements of voxel-wise segmentation methods if these lesions are included in the segmentation process. Several authors have presented different techniques to improve brain tissue volume estimations by filling WM lesions...
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| Format: | Article |
| Language: | English |
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Elsevier
2014-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158214001259 |
| _version_ | 1818062792663498752 |
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| author | Sergi Valverde Arnau Oliver Xavier Lladó |
| author_facet | Sergi Valverde Arnau Oliver Xavier Lladó |
| author_sort | Sergi Valverde |
| collection | DOAJ |
| description | Multiple sclerosis white matter (WM) lesions can affect brain tissue volume measurements of voxel-wise segmentation methods if these lesions are included in the segmentation process. Several authors have presented different techniques to improve brain tissue volume estimations by filling WM lesions before segmentation with intensities similar to those of WM. Here, we propose a new method to refill WM lesions, where contrary to similar approaches, lesion voxel intensities are replaced by random values of a normal distribution generated from the mean WM signal intensity of each two-dimensional slice. We test the performance of our method by estimating the deviation in tissue volume between a set of 30 T1-w 1.5 T and 30 T1-w 3 T images of healthy subjects and the same images where: WM lesions have been previously registered and afterwards replaced their voxel intensities to those between gray matter (GM) and WM tissue. Tissue volume is computed independently using FAST and SPM8. When compared with the state-of-the-art methods, on 1.5 T data our method yields the lowest deviation in WM between original and filled images, independently of the segmentation method used. It also performs the lowest differences in GM when FAST is used and equals to the best method when SPM8 is employed. On 3 T data, our method also outperforms the state-of-the-art methods when FAST is used while performs similar to the best method when SPM8 is used. The proposed technique is currently available to researchers as a stand-alone program and as an SPM extension. |
| first_indexed | 2024-12-10T14:09:50Z |
| format | Article |
| id | doaj.art-96878d25eda34a87a8d866818875a5b7 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-10T14:09:50Z |
| publishDate | 2014-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-96878d25eda34a87a8d866818875a5b72022-12-22T01:45:32ZengElsevierNeuroImage: Clinical2213-15822014-01-016C869210.1016/j.nicl.2014.08.016A white matter lesion-filling approach to improve brain tissue volume measurementsSergi Valverde0Arnau Oliver1Xavier Lladó2Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, Girona 17071, SpainComputer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, Girona 17071, SpainComputer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, Girona 17071, SpainMultiple sclerosis white matter (WM) lesions can affect brain tissue volume measurements of voxel-wise segmentation methods if these lesions are included in the segmentation process. Several authors have presented different techniques to improve brain tissue volume estimations by filling WM lesions before segmentation with intensities similar to those of WM. Here, we propose a new method to refill WM lesions, where contrary to similar approaches, lesion voxel intensities are replaced by random values of a normal distribution generated from the mean WM signal intensity of each two-dimensional slice. We test the performance of our method by estimating the deviation in tissue volume between a set of 30 T1-w 1.5 T and 30 T1-w 3 T images of healthy subjects and the same images where: WM lesions have been previously registered and afterwards replaced their voxel intensities to those between gray matter (GM) and WM tissue. Tissue volume is computed independently using FAST and SPM8. When compared with the state-of-the-art methods, on 1.5 T data our method yields the lowest deviation in WM between original and filled images, independently of the segmentation method used. It also performs the lowest differences in GM when FAST is used and equals to the best method when SPM8 is employed. On 3 T data, our method also outperforms the state-of-the-art methods when FAST is used while performs similar to the best method when SPM8 is used. The proposed technique is currently available to researchers as a stand-alone program and as an SPM extension.http://www.sciencedirect.com/science/article/pii/S2213158214001259BrainMRIMultiple sclerosisTissue segmentationWhite matter lesionsLesion-filling |
| spellingShingle | Sergi Valverde Arnau Oliver Xavier Lladó A white matter lesion-filling approach to improve brain tissue volume measurements NeuroImage: Clinical Brain MRI Multiple sclerosis Tissue segmentation White matter lesions Lesion-filling |
| title | A white matter lesion-filling approach to improve brain tissue volume measurements |
| title_full | A white matter lesion-filling approach to improve brain tissue volume measurements |
| title_fullStr | A white matter lesion-filling approach to improve brain tissue volume measurements |
| title_full_unstemmed | A white matter lesion-filling approach to improve brain tissue volume measurements |
| title_short | A white matter lesion-filling approach to improve brain tissue volume measurements |
| title_sort | white matter lesion filling approach to improve brain tissue volume measurements |
| topic | Brain MRI Multiple sclerosis Tissue segmentation White matter lesions Lesion-filling |
| url | http://www.sciencedirect.com/science/article/pii/S2213158214001259 |
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