Abstract
Purpose: Lesion volume is a meaningful measure in multiple sclerosis (MS) prognosis. Manual lesion segmentation for computing volume in a single or multiple time points is time consuming and suffers from intra and inter-observer variability.Methods: In this paper, we present MSmetrix-long: a joint expectation-maximization (EM) framework for two time point white matter (WM) lesion segmentation. MSmetrix-long takes as input a 3D T1-weighted and a 3D FLAIR MR image and segments lesions in three steps: (1) cross-sectional lesion segmentation of the two time points; (2) creation of difference image, which is used to model the lesion evolution; (3) a joint EM lesion segmentation framework that uses output of step (1) and step (2) to provide the final lesion segmentation. The accuracy (Dice score) and reproducibility (absolute lesion volume difference) of MSmetrix-long is evaluated using two datasets.Results: On the first dataset, the median Dice score between MSmetrix-long and expert lesion segmentation was 0.63 and the Pearson correlation coefficient (PCC) was equal to 0.96. On the second dataset, the median absolute volume difference was 0.11 ml.Conclusions: MSmetrix-long is accurate and consistent in segmenting MS lesions. Also, MSmetrix-long compares favorably with the publicly available longitudinal MS lesion segmentation algorithm of Lesion Segmentation Toolbox.
Highlights
Accurate and reliable lesion segmentation based on brain MRI scans is valuable for the diagnosis and monitoring of disease activity in patients with Multiple Sclerosis (MS) (Blystad et al, 2016; Deeks, 2016)
We present MSmetrix-long: an iterative white matter (WM) lesion segmentation method based on a joint EM framework that takes as input clinically acquired 3D T1weighted and 3D fluid-attenuated inversion recovery (FLAIR) images of two time points
The pipeline has four steps: (1) Cross-sectional analysis, that segments the individual time points into gray matter (GM), WM, cerebro-spinal fluid (CSF), and lesions, (2) FLAIR based difference image, which is created by subtracting the FLAIR images of both time points after bias correction, co-registration and intensity normalization, (3) Joint lesion segmentation, that aims to improve the individual time point lesion segmentation using the other time point information on tissue and lesion segmentation and difference image obtained from step-2, (4) a pruning step, that refines the lesion segmentation obtained in the step-3 to eliminate non-lesions candidates
Summary
Accurate and reliable lesion segmentation based on brain MRI scans is valuable for the diagnosis and monitoring of disease activity in patients with Multiple Sclerosis (MS) (Blystad et al, 2016; Deeks, 2016). Expert manual delineation of lesions is considered as the gold standard, it is time consuming and often suffers from intra and inter observer variability (Erbayat Altay et al, 2013). To alleviate this problem, several automatic methods have been proposed in the literature to segment MS lesions. Executing a cross-sectional method for each time point would produce the longitudinal measures of interest, but such measures are less reliable as each time point is processed independently. Pre-processing of input MR images in these three groups is generally performed and consists of registration to a reference image or a common space, skull stripping, bias field correction and intensity normalization
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