Evaluating the Effects of White Matter Multiple Sclerosis Lesions on the Volume Estimation of 6 Brain Tissue Segmentation Methods

Valverde, Sergi; Oliver, Arnau; Diéz, Yago; Cabezas, Mariano; Vilanova, Joan C.; Ramió-Torrentà, Lluı́s; Lladó, Xavier

doi:10.3174/ajnr.a4262

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…Because of that, in an attempt to evaluate this effect, we generated a set of synthetic images as done in (Battaglini et al, 2012, Chard et al, 2010, Gelineau-Morel et al, 2012, Nakamura and Fisher, 2009) to evaluate the effect of the lesions on tissue segmentation. The effect of such lesions on automatic tissue segmentation strategies has been widely evaluated, and also strategies to reduce this effect, such as masking out the lesions or fill them before segmentation, have been proposed (Battaglini et al, 2012, Chard et al, 2010, Valverde et al, 2015). …”

Section: Discussionmentioning

confidence: 99%

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

González-Villà

Valverde

Cabezas

et al. 2017

NeuroImage: Clinical

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In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS) lesions on three well-known automatic brain structure segmentation methods, namely, FreeSurfer, FIRST and multi-atlas fused by majority voting, which use learning-based, deformable and atlas-based strategies, respectively. To perform a quantitative analysis, 100 synthetic images of MS patients with a total of 2174 lesions are simulated on two public databases with available brain structure ground truth information (IBSR18 and MICCAI’12). The Dice similarity coefficient (DSC) differences and the volume differences between the healthy and the simulated images are calculated for the subcortical structures and the brainstem. We observe that the three strategies are affected when lesions are present. However, the effects of the lesions do not follow the same pattern; the lesions either make the segmentation method underperform or surprisingly augment the segmentation accuracy. The obtained results show that FreeSurfer is the method most affected by the presence of lesions, with DSC differences (generated − healthy) ranging from − 0.11 ± 0.54 to 9.65 ± 9.87, whereas FIRST tends to be the most robust method when lesions are present (− 2.40 ± 5.54 to 0.44 ± 0.94). Lesion location is not important for global strategies such as FreeSurfer or majority voting, where structure segmentation is affected wherever the lesions exist. On the other hand, FIRST is more affected when the lesions are overlaid or close to the structure of analysis. The most affected structure by the presence of lesions is the nucleus accumbens (from − 1.12 ± 2.53 to 1.32 ± 4.00 for the left hemisphere and from − 2.40 ± 5.54 to 9.65 ± 9.87 for the right hemisphere), whereas the structures that show less variation include the thalamus (from 0.03 ± 0.35 to 0.74 ± 0.89 and from − 0.48 ± 1.08 to − 0.04 ± 0.22) and the brainstem (from − 0.20 ± 0.38 to 1.03 ± 1.31). The three segmentation approaches are affected by the presence of MS lesions, which demonstrates that there exists a problem in the automatic segmentation methods of the deep gray matter (DGM) structures that has to be taken into account when using them as a tool to measure the disease progression.

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Section: Discussionmentioning

confidence: 99%

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

González-Villà

Valverde

Cabezas

et al. 2017

NeuroImage: Clinical

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“…The effect of lesions on total tissue volume was partly limited due to the canceling effect between the errors produced in normal-appearing tissue and the number of lesion voxels that were segmented as GM ( Valverde et al, 2015b ). This aspect is relevant because it explains why the observed % of error in total tissue volume was small or not significant between the evaluated pipelines of our study, even within the Original images intentionally segmented containing lesions.…”

Section: Discussionmentioning

confidence: 99%

“…However, existing differences for a particular method in different studies may be influenced by the same image data, imaging hardware and acquisition parameters ( Clark et al, 2006 ). Furthermore, several authors have reported that the inclusion of WM lesions in tissue segmentation can affect significantly the accuracy of these techniques ( Battaglini et al, 2012 , Nakamura and Fisher, 2009 , Valverde et al, 2015b ), leading to the development of different preprocessing strategies to fill lesion regions with signal intensities similar to WM before tissue segmentation ( Battaglini et al, 2012 , Chard et al, 2010 , Valverde et al, 2014 ). So far, in all the lesion filling approaches, MS lesions have to be delineated first, usually by their manual annotation, which is a tedious, challenging and time-consuming task ( Sanfilipo et al, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

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

Valverde

Oliver

Roura

et al. 2015

NeuroImage: Clinical

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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.

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“…For automatically segmented lesions, the LST lesion filling module was used for in‐painting. The underlying algorithm of T1 lesion filling based on automatic T2 FLAIR lesion segmentation is inspired by a method proposed by Chard and colleagues, where candidate region voxels were replaced by random intensities from a Gaussian distribution generated from the normal‐appearing WM intensities and then filtered to reintroduce the original spatial variation in WM …”

Section: Methodsmentioning

confidence: 99%

The Impact of MRI T1 Hypointense Brain Lesions on Cerebral Deep Gray Matter Volume Measures in Multiple Sclerosis

Buyukturkoglu

Mormina

Jager

et al. 2019

Journal of Neuroimaging

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BACKGROUND AND PURPOSE: Deep gray matter (DGM) atrophy has been shown at early stages of multiple sclerosis (MS) and reported as an informative marker of cognitive dysfunction and clinical progression. Therefore, accurate measurement of DGM structure volume is a key priority in MS research. Findings from prior studies have shown that hypointense T1 lesions may impact the accuracy of global brain volume measures; however, literature on the effects of hypointense T1 lesions on DGM structure volumes is sparse. METHODS: We explored the effects of hypointense T1 lesions on data from 54 relapsing remitting MS patients. Lesions were segmented both manually and with a freely available automatic lesion segmentation/in-painting algorithm (Lesion Segmentation Tool-LST). Volumes of 14 DGM structures were calculated from non-in-painted and in-painted images and compared via paired t-tests, intraclass correlation coefficient, and Dice similarity coefficient.RESULTS: There were no significant differences in DGM structural volumes between non-in-painted and in-painted images. Automatic lesion-segmentation/in-painting tool provided similar results to manual segmentation/in-painting. CONCLUSIONS: Our results suggest that lesion in-painting has a negligible impact on DGM structure volume measurement although some regions are more vulnerable to the impact of lesions than others. Furthermore, manual lesion segmentation/inpainting can be replaced by an automatic segmentation/in-painting process.

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Evaluating the Effects of White Matter Multiple Sclerosis Lesions on the Volume Estimation of 6 Brain Tissue Segmentation Methods

Cited by 12 publications

References 19 publications

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation

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

The Impact of MRI T1 Hypointense Brain Lesions on Cerebral Deep Gray Matter Volume Measures in Multiple Sclerosis

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