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A white matter lesion-filling approach to improve brain tissue volume measurements.

Valverde S, Oliver A, Lladó X - Neuroimage Clin (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, Girona 17071, Spain.

ABSTRACT
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.

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Related in: MedlinePlus

Proposed algorithm for filling WM lesions. From a preprocessed T1-w image (skull-stripping and intensity inhomogeneity corrected), WM lesions are masked out using the provided WM lesion binary mask. Using a Fuzzy-C-means approach, we estimate the probability of each image voxel to be classified as CSF, GM, and NAWM. For each slice composing the whole image, lesion voxel intensities are replaced by a random intensity derived from a normal distribution with mean and half of the standard deviation of the NAWM tissue intensities of the current slice.
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f0005: Proposed algorithm for filling WM lesions. From a preprocessed T1-w image (skull-stripping and intensity inhomogeneity corrected), WM lesions are masked out using the provided WM lesion binary mask. Using a Fuzzy-C-means approach, we estimate the probability of each image voxel to be classified as CSF, GM, and NAWM. For each slice composing the whole image, lesion voxel intensities are replaced by a random intensity derived from a normal distribution with mean and half of the standard deviation of the NAWM tissue intensities of the current slice.

Mentions: The proposed method aims to combine the global approach of Chard et al. (2010) with the similarity between refilled voxel intensities and their surrounding voxels of local methods such as Battaglini et al. (2012) and Magon et al. (2013). Basically, for each slice composing the three-dimensional image, lesion voxel intensities are replaced by random intensities of a normal distribution generated from the mean NAWM intensity of the current slice. Fig. 1 summarizes the lesion-filling process graphically.


A white matter lesion-filling approach to improve brain tissue volume measurements.

Valverde S, Oliver A, Lladó X - Neuroimage Clin (2014)

Proposed algorithm for filling WM lesions. From a preprocessed T1-w image (skull-stripping and intensity inhomogeneity corrected), WM lesions are masked out using the provided WM lesion binary mask. Using a Fuzzy-C-means approach, we estimate the probability of each image voxel to be classified as CSF, GM, and NAWM. For each slice composing the whole image, lesion voxel intensities are replaced by a random intensity derived from a normal distribution with mean and half of the standard deviation of the NAWM tissue intensities of the current slice.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4215527&req=5

f0005: Proposed algorithm for filling WM lesions. From a preprocessed T1-w image (skull-stripping and intensity inhomogeneity corrected), WM lesions are masked out using the provided WM lesion binary mask. Using a Fuzzy-C-means approach, we estimate the probability of each image voxel to be classified as CSF, GM, and NAWM. For each slice composing the whole image, lesion voxel intensities are replaced by a random intensity derived from a normal distribution with mean and half of the standard deviation of the NAWM tissue intensities of the current slice.
Mentions: The proposed method aims to combine the global approach of Chard et al. (2010) with the similarity between refilled voxel intensities and their surrounding voxels of local methods such as Battaglini et al. (2012) and Magon et al. (2013). Basically, for each slice composing the three-dimensional image, lesion voxel intensities are replaced by random intensities of a normal distribution generated from the mean NAWM intensity of the current slice. Fig. 1 summarizes the lesion-filling process graphically.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, Girona 17071, Spain.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus