Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE

Francesco La Rosa,Ahmed Abdulkadir,Mário João Fartaria,Reza Rahmanzadeh,Po-Jui Lu,Riccardo Galbusera,Muhamed Barakovic,Jean-Philippe Thiran,Cristina Granziera,Merixtell Bach Cuadra

doi:10.1016/j.nicl.2020.102335

Francesco La Rosa, Ahmed Abdulkadir + Show 8 more

Open Access

https://doi.org/10.1016/j.nicl.2020.102335

Copy DOI

Abstract

The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortical lesions are hardly visible in conventional magnetic resonance imaging (MRI) at 3T, and thus their automated detection has been so far little explored. In this study, we propose a fully-convolutional deep learning approach, based on the 3D U-Net, for the automated segmentation of cortical and white matter lesions at 3T. For this purpose, we consider a clinically plausible MRI setting consisting of two MRI contrasts only: one conventional T2-weighted sequence (FLAIR), and one specialized T1-weighted sequence (MP2RAGE). We include 90 patients from two different centers with a total of 728 and 3856 gray and white matter lesions, respectively. We show that two reference methods developed for white matter lesion segmentation are inadequate to detect small cortical lesions, whereas our proposed framework is able to achieve a detection rate of 76% for both cortical and white matter lesions with a false positive rate of 29% in comparison to manual segmentation. Further results suggest that our framework generalizes well for both types of lesion in subjects acquired in two hospitals with different scanners.

Highlights

Multiple Sclerosis (MS) is a chronic demyelinating disease involving the central nervous system (CNS)
The median of all metrics obtained in the test folds of the crossvalidation are shown in Table 1. 3D U-Net- achieves a 75% cortical lesions (CLs) detection rate and the best performance in all metrics except for the positive predicted value (PPV), for which LST-LGA has the best value (71%)
The proposed prototype 3D U-Net- outperformed the baseline methods and proved to generalize well for cases acquired with a different scanner

Summary

Introduction

Multiple Sclerosis (MS) is a chronic demyelinating disease involving the central nervous system (CNS). MS is characterized by sharply delimited lesional areas with primary demyelination, axonal loss, and reactive gliosis, both in the white and in the grey matter. The pathological process is not confined to these macroscopically visible focal areas but is generalized in the entire central nervous system (Compston and Coles, 2008; Kuhlmann et al., 2017). The current MS diagnostic criteria (McDonald criteria (Thompson et al, 2018)) are based on the count and location of lesions in MRI. Common MRI protocols currently include T1-weighted (T1w), T2-weighted (T2w), and fluid-attenuated inversion recovery T2 (FLAIR) sequences. The main focus in research and clinical practice has been set on white matter lesions (WMLs), clearly visible in the above-mentioned conventional MRI sequences.

Objectives

Methods

Results

Conclusion