DUGi

A toolbox for multiple sclerosis lesion segmentation

http://dugi.udg.edu/item/http:@@@@hdl.handle.net@@2072@@297879

Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images. Methods: Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image. Results: The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches. Conclusion: Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities

E. Roura holds a BRUdG2013 grant. S. Valverde holds a FI-DGR2013 grant from the Generalitat de Catalunya. This work has been partially supported by BLa Fundació la Marató de TV3^ and by Retos de Investigación TIN2014-55710-R

This work has been partially supported by BLa Fundació la Marató de TV3^ and by Retos de Investigación TIN2014-55710-R

Elsevier