Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-wMultiple 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 ormanual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) andwhite matter (WM) using SPM8, and compared with the same images where expert lesion annotationswere filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error inGMandWMvolumeon images ofMS 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 inGMandWMvolume. However, the % of error was significantly lower when automatically estimated lesionswere 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

S. Valverde holds a FI-DGR2013 grant from the Generalitat de Catalunya. E. Roura holds a BR-UdG2013 grant. This work has been partially supported by “La Fundació la Marató de TV3” and by Retos de Investigación TIN2014-55710-R