Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation

Sudre, Carole H.; Cardoso, M. Jorge; Bouvy, Willem H.; Biessels, Geert Jan; Barnes, Josephine; Ourselin, Sébastien

doi:10.1109/tmi.2015.2419072

Cited by 138 publications

(111 citation statements)

References 82 publications

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“…This precluded quantitative comparison of interval change in WMH between scans. Regional quantification of WMH in the cadaveric MRI FLAIR sequence was performed using segmentation and localization analyses as previously described (Sudre et al, 2017, 2015; Sudre, Cardoso, & Ourselin, 2017) (see Supplementary Material). This allowed quantification of WMH burden in each lobe and within different cortical and subcortical “layers”, guiding selection of regions for histological analysis.…”

Section: Methodsmentioning

confidence: 99%

Pathological correlates of white matter hyperintensities in a case of progranulin mutation associated frontotemporal dementia

et al. 2018

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White matter hyperintensities (WMH) are often seen on MRI brain scans in frontotemporal dementia (FTD) due to progranulin (GRN) mutations, but their pathological correlates are unknown. We examined the histological changes underlying WMH in a patient with GRN mutation associated behavioral variant FTD. In vivo and cadaveric MRI showed progressive, asymmetric frontotemporal and parietal atrophy, and asymmetrical WMH predominantly affecting frontal mid-zones. We first performed segmentation and localization analyses of WMH present on cadaveric MRI FLAIR images, then selected five different brain regions directly matched to differing severities of WMH for histological analysis. We used immunohistochemistry to assess vascular pathology, degree of spongiosis, neuronal and axonal loss, TDP-43, demyelination and astrogliosis, and microglial burden and morphology. Brain regions with significant WMH displayed severe cortical and white matter pathology, and prominent white matter microglial activation and microglial dystrophy, but only mild axonal loss and minimal vascular pathology. Our study suggests that WMH in GRN mutation carriers are not secondary to vascular pathology. Whilst cortical pathology induced axonal degeneration could contribute to white matter damage, individuals with GRN mutations could develop selective white matter vulnerability and myelin loss due to chronic, regional microglial dysfunction arising from GRN haploinsufficiency.

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Section: Methodsmentioning

confidence: 99%

Pathological correlates of white matter hyperintensities in a case of progranulin mutation associated frontotemporal dementia

et al. 2018

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“…Quantitative WMH load was estimated jointly from both 3D T1 and 3D FLAIR scans using a previously described algorithm (Sudre et al, 2015). Details of the WMH lesion segmentation are provided in the Supplementary Methods.…”

Section: Mri Analysismentioning

confidence: 99%

White matter hyperintensities and vascular risk factors in monozygotic twins

Kate

Sudre

Braber

et al. 2018

Neurobiology of Aging

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a b s t r a c tCerebral white matter hyperintensities (WMHs) have been associated with vascular risk factors, both of which are under genetic influence. We examined in a monozygotic twin sample whether the association between vascular risk and WMHs is influenced by overlapping genetic factors. We included 195 cognitively normal monozygotic twins (age ¼ 70 AE 7 years), including 94 complete pairs. Regional WMH load was estimated using an automated algorithm. Vascular risk was summarized with the Framingham score. The withinetwin pair correlation for total WMHs was 0.76 and for Framingham score was 0.77. Within participants, Framingham score was associated with total and periventricular WMHs (r ¼ 0.32). Framingham score in 1 twin was also associated with total WMHs in the co-twin (r ¼ 0.26). Up to 83% of the relation between both traits could be explained by shared genetic effects. In conclusion, monozygotic twins have highly similar vascular risk and WMH burden, confirming a genetic background for these traits. The association between both traits is largely driven by overlapping genetic factors.

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“…A variety of techniques are being used for automated MS lesion segmentation (Anbeek et al, 2004; Brosch et al, 2015, 2016; Deshpande et al, 2015; Dugas-Phocion et al, 2004; Elliott et al, 2013, 2014; Ferrari et al, 2003; Geremia et al, 2010; Havaei et al, 2016; Jain et al, 2015; Jog et al, 2015; Johnston et al, 1996; Kamber et al, 1996; Khayati et al, 2008; Rey et al, 1999, 2002; Roy et al, 2010, 2014b; Schmidt et al, 2012; Shiee et al, 2010; Subbanna et al, 2015; Sudre et al, 2015; Tomas-Fernandez and Warfield, 2011, 2012; Valverde et al, 2017; Weiss et al, 2013; Welti et al, 2001; Xie and Tao, 2011) with several review articles available that describe and evaluate the utility of these methods (García-Lorenzo et al, 2013; Lladó et al, 2012), though semi-automated approaches have also been reported (Udupa et al, 1997; Wu et al, 2006; Zijdenbos et al, 1994). The early work on WML segmentation used the principle of modeling the distributions of intensities of healthy brain tissues and segmenting outliers to those distributions as lesions.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

et al. 2017

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In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: 1) the sharing of a rich data set; 2) collaboration and comparison of the various avenues of research being pursued in the community; and 3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website 1 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

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Bayesian Model Selection for Pathological Neuroimaging Data Applied to White Matter Lesion Segmentation

Cited by 138 publications

References 82 publications

Pathological correlates of white matter hyperintensities in a case of progranulin mutation associated frontotemporal dementia

Pathological correlates of white matter hyperintensities in a case of progranulin mutation associated frontotemporal dementia

White matter hyperintensities and vascular risk factors in monozygotic twins

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

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