Comparison of Manual Cross-Sectional Measurements and Automatic Volumetry of the Corpus Callosum, and Their Clinical Impact: A Study on Type 1 Diabetes and Healthy Controls

Claesson, Tor-Björn; Putaala, Jukka; Shams, Sara; Salli, Eero; Gordin, Daniel; Liebkind, Ron; Forsblom, Carol; Summanen, Paula; Tatlısumak, Turgut; Groop, Per‐Henrik; Martola, Juha; Thorn, Lena M.

doi:10.3389/fneur.2020.00027

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…There are several methods for segmenting the corpus callosum based on MRI, including manual segmentations, 9 atlas‐based software, such as FreeSurfer, 10 and machine learning algorithms 11 . FreeSurfer can render 3D segmentations of the corpus callosum with high precision but lower accuracy, as compared to manual delineations 12 .…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis

Plattén

Brusini

Andersson

et al. 2021

Journal of Neuroimaging

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Background and Purpose Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D‐based segmentations. We developed a supervised machine learning algorithm, DeepnCCA, for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine. Methods In a prospective study of 553 MS patients with 704 acquisitions, 200 unique 2D T2‐weighted MRI scans were delineated to develop, train, and validate DeepnCCA. Comparative FreeSurfer segmentations were obtained in 504 3D T1‐weighted scans. Both FreeSurfer and DeepnCCA outputs were correlated with clinical disability. Using principal component analysis of the DeepnCCA output, the morphological changes were explored in relation to clinical disease burden. Results DeepnCCA and manual segmentations had high similarity (Dice coefficients 98.1±.11%, 89.3±.76%, for intracranial and corpus callosum area, respectively through 10‐fold cross‐validation). DeepnCCA had numerically stronger correlations with cognitive and physical disability as compared to FreeSurfer: Expanded disability status scale (EDSS) ±6 months (r = –.22 P = .002; r = –.17, P = .013), future EDSS (r = –.26, P<.001; r = –.17, P = .012), and future symbol digit modalities test (r = .26, P = .001; r = .24, P = .003). The corpus callosum became thinner with increasing cognitive and physical disability. Increasing physical disability, additionally, significantly correlated with a more angled corpus callosum. Conclusions DeepnCCA (https://github.com/plattenmichael/DeepnCCA/) is an openly available tool that can provide fast and accurate corpus callosum measurements applicable to large MS cohorts, potentially suitable for monitoring disease progression and therapy response.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis

Plattén

Brusini

Andersson

et al. 2021

Journal of Neuroimaging

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“…For white matter hyperintensities, having more than one hyperintense lesion was considered a significant burden. Cerebral microbleeds were further categorized as zero, one to two, or three or more because having > 2 microbleeds was previously linked to more retinopathy and a smaller callosal cross-sectional area in our cohort [ 12 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Cerebral Small Vessel Disease Is Associated With Smaller Brain Volumes in Adults With Type 1 Diabetes

Claesson,

Putaala,

Shams

et al. 2024

Journal of Diabetes Research

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Introduction: Type 1 diabetes has been linked to brain volume reductions as well as to cerebral small vessel disease (cSVD). This study concerns the relationship between normalized brain volumes (volume fractions) and cSVD, which has not been examined previously.Methods: We subjected brain magnetic resonance imaging studies of 187 adults of both sexes with Type 1 diabetes and 30 matched controls to volumetry and neuroradiological interpretation.Results: Participants with Type 1 diabetes had smaller thalami compared to controls without diabetes (p = 0.034). In subgroup analysis of the Type 1 diabetes group, having any sign of cSVD was associated with smaller cortical (p = 0.031) and deep gray matter volume fractions (p = 0.029), but a larger white matter volume fraction (p = 0.048). After correcting for age, the smaller putamen volume remained significant.Conclusions: We found smaller thalamus volume fractions in individuals with Type 1 diabetes as compared to those without diabetes, as well as reductions in brain volume fractions related to signs of cSVD in individuals with Type 1 diabetes.

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Comparison of Manual Cross-Sectional Measurements and Automatic Volumetry of the Corpus Callosum, and Their Clinical Impact: A Study on Type 1 Diabetes and Healthy Controls

Abstract: Conclusion: Manually measured callosal area and automatically segmented are interchangeable. The association seen between callosal size with cerebral microbleeds and insulin resistance is indicative of small vessel disease pathology in diabetes type 1.

Cited by 2 publications

References 31 publications

Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis

Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis

Cerebral Small Vessel Disease Is Associated With Smaller Brain Volumes in Adults With Type 1 Diabetes

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