Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

Abstract

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.

Mental speed is associated with the shape irregularity of white matter MRI hyperintensity load

“…In the present study the lesion segmentation toolbox (LST) was used for this purpose. Although this toolbox was proposed for segmentation of demyelinating lesions in subjects with multiple sclerosis (Valverde et al 2015; Muhlau et al 2013; Mazerolle et al 2013), the algorithm provided reliable segmentation of WMH in both samples of elderly subjects included in the present study. The LST failed according to visual inspection in only 6 of 285 cases (2.1 %).…”

Unraveling treatment response in multiple sclerosis

“…Although methods for precise quantitative analyses of MRI lesion already exist and have been used in clinical trials for many years, strong efforts are being made to develop automated quantitative lesion segmentation by dedicated software with the aim of increasing accuracy in lesion detection while decreasing time of analyses. [58][59][60] Application of automated MRI subtraction can improve interpretation of serial imaging, but this approach cannot be incorporated into daily clinical practice until validation studies are performed and technical improvements are achieved.…”

A review on brain structures segmentation in magnetic resonance imaging

“…Ms ing automatic lesion segmentation and filling on automatic tissue segmentation pipelines has recently been studied [169], showing very similar results to that of manually segmenting the lesions. This has not yet been integrated as part of any automatic brain structures segmentation pipeline and indeed opens new challenges to the research community.…”

“…To the best of our knowledge, how white matter lesions, such as the ones produced in multiple sclerosis or lupus, affect these algorithms has not yet been evaluated. Nevertheless, it is a well known problem among automatic tissue segmentation methods [169], where lesion filling techniques [170][171][172] have already been applied improving the accuracy of tissue volume [173,174]. As far as we know, these techniques have not yet been evaluated in combination with automatic brain structure segmentation algorithms and making these algorithms robust against lesions remains still an open challenge to the research community.…”