Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling
Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been...
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doaj-13a2bfe25d104681b78206a8d938ac2e2020-11-24T21:06:07ZengElsevierNeuroImage: Clinical2213-15822015-01-019C64064710.1016/j.nicl.2015.10.012Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and fillingSergi Valverde0Arnau Oliver1Eloy Roura2Deborah Pareto3Joan C. Vilanova4Lluís Ramió-Torrentà5Jaume Sastre-Garriga6Xavier Montalban7Àlex Rovira8Xavier Lladó9Dept. of Computer Architecture and Technology, University of Girona, SpainDept. of Computer Architecture and Technology, University of Girona, SpainDept. of Computer Architecture and Technology, University of Girona, SpainMagnetic Resonance Unit, Dept. of Radiology, Vall d'Hebron University Hospital, Spain Architecture and Technology, University of Girona, SpainGirona Magnetic Resonance Center, SpainMultiple Sclerosis and Neuro-immunology Unit, Dr. Josep Trueta University Hospital, SpainNeurology Unit, Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d'Hebron University Hospital, SpainNeurology Unit, Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d'Hebron University Hospital, SpainMagnetic Resonance Unit, Dept. of Radiology, Vall d'Hebron University Hospital, Spain Architecture and Technology, University of Girona, SpainDept. of Computer Architecture and Technology, University of Girona, SpainLesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.http://www.sciencedirect.com/science/article/pii/S2213158215300127BrainMultiple sclerosisMRIBrain atrophyAutomated tissue segmentationWhite matter lesionsLesion filling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sergi Valverde Arnau Oliver Eloy Roura Deborah Pareto Joan C. Vilanova Lluís Ramió-Torrentà Jaume Sastre-Garriga Xavier Montalban Àlex Rovira Xavier Lladó |
spellingShingle |
Sergi Valverde Arnau Oliver Eloy Roura Deborah Pareto Joan C. Vilanova Lluís Ramió-Torrentà Jaume Sastre-Garriga Xavier Montalban Àlex Rovira Xavier Lladó Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling NeuroImage: Clinical Brain Multiple sclerosis MRI Brain atrophy Automated tissue segmentation White matter lesions Lesion filling |
author_facet |
Sergi Valverde Arnau Oliver Eloy Roura Deborah Pareto Joan C. Vilanova Lluís Ramió-Torrentà Jaume Sastre-Garriga Xavier Montalban Àlex Rovira Xavier Lladó |
author_sort |
Sergi Valverde |
title |
Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling |
title_short |
Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling |
title_full |
Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling |
title_fullStr |
Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling |
title_full_unstemmed |
Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling |
title_sort |
quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling |
publisher |
Elsevier |
series |
NeuroImage: Clinical |
issn |
2213-1582 |
publishDate |
2015-01-01 |
description |
Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations. |
topic |
Brain Multiple sclerosis MRI Brain atrophy Automated tissue segmentation White matter lesions Lesion filling |
url |
http://www.sciencedirect.com/science/article/pii/S2213158215300127 |
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