Lesion filling effect in regional brain volume estimations: a study in multiple sclerosis patients with low lesion load

Abstract

Introduction

Regional brain volume estimation in multiple sclerosis (MS) patients is prone to error due to white matter lesions being erroneously segmented as grey matter. The Lesion Segmentation Toolbox (LST) is an automatic tool that estimates a lesion mask based on 3D T2-FLAIR images and then uses this mask to fill the structural MRI image. The goal of this study was (1) to test the LST for estimating white matter lesion volume in a cohort of MS patients using 2D T2-FLAIR images, and (2) to evaluate the performance of the optimized LST on image segmentation and the impact on the calculated grey matter fraction (GMF).

Methods

The study included 110 patients with a clinically isolated syndrome and 42 with a relapsing-remitting MS scanned on a 3.0-T MRI system. In a subset of consecutively selected patients, the lesion mask was semi-manually delineated over T2-FLAIR images. After establishing the optimized LST parameters, the corresponding regional fractions were calculated for the original, filled, and masked images.

Results

A high agreement (intraclass correlation coefficient (ICC) = 0.955) was found between the (optimized) LST and the semi-manual lesion volume estimations. The GMF was significantly smaller when lesions were masked (mean difference −0.603, p < 0.001) or when the LST filling technique was used (mean difference −0.598, p < 0.001), compared to the GMF obtained from the original image.

Conclusion

LST lesion volume calculation seems reliable. GMFs are significantly reduced when a method to correct the contribution of MS lesions is used, and it may have an impact in assessing GMF differences between clinical cohorts.

from #Head and Neck by Sfakianakis via simeraentaxei on Inoreader http://ift.tt/1K2Dtgq
via IFTTT

from #Med Blogs by Alexandros G.Sfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/1K35LHx
via IFTTT