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Alexandra de Sitter

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5 papers

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YNICL Journal 2021 Journal Article

Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references

Alexandra de Sitter
Jessica Burggraaff
Fabian Bartel
Miklos Palotai
Yaou Liu
Jorge Simoes
Serena Ruggieri
Katharina Schregel

BACKGROUND: Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. OBJECTIVES: This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). METHODS: A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. RESULTS: All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. CONCLUSIONS: The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload.

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YNICL Journal 2021 Journal Article

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

Jessica Burggraaff
Yao Liu
Juan C. Prieto
Jorge Simoes
Alexandra de Sitter
Serena Ruggieri
Iman Brouwer
Birgit I. Lissenberg-Witte

BACKGROUND AND RATIONALE: Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. METHODS: Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. RESULTS: In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. CONCLUSION: Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.

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YNICL Journal 2018 Journal Article

Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI

Houshang Amiri
Alexandra de Sitter
Kerstin Bendfeldt
Marco Battaglini
Claudia A.M. Gandini Wheeler-Kingshott
Massimiliano Calabrese
Jeroen J.G. Geurts
Maria A. Rocca

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.

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YNICL Journal 2017 Journal Article

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy

Martijn D. Steenwijk
Houshang Amiri
Menno M. Schoonheim
Alexandra de Sitter
Frederik Barkhof
Petra J.W. Pouwels
Hugo Vrenken

INTRODUCTION: MRI data. METHODS: data from 100 MS patients and 20 matched healthy controls. In fifty of the MS patients a second time point was available. In this subgroup, we additionally analyzed the whole-brain and GM volume change using the longitudinal pipeline of MSmetrix and compared the results with those of FreeSurfer (longitudinal pipeline) and SIENA. RESULTS: . Among the cross-sectional methods, Deming regression analyses revealed proportional errors particularly in MSmetrix and SPM. The mean difference percentage brain volume change (PBVC) was lowest between longitudinal MSmetrix and SIENA (+ 0.16 ± 0.91%). A strong proportional error was present between longitudinal percentage gray matter volume change (PGVC) measures of MSmetrix and FreeSurfer (slope = 2.48). All longitudinal methods were sensitive to the MRI hardware upgrade that occurred during the time of the study. CONCLUSION: MSmetrix, FreeSurfer, FSL and SPM show differences in atrophy measurements, even at the whole-brain level, that are large compared to typical atrophy rates observed in MS. Especially striking are the proportional errors between methods. Cross-sectional MSmetrix behaved similarly to SPM, both in terms of mean volume difference as well as proportional error. Longitudinal MSmetrix behaved most similar to SIENA. Our results indicate that brain volume measurement and normalization from T1-weighted images remains an unsolved problem that requires much more attention.

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YNIMG Journal 2017 Journal Article

Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study

Alexandra de Sitter
Martijn D. Steenwijk
Aurélie Ruet
Adriaan Versteeg
Yaou Liu
Ronald A. van Schijndel
Petra J.W. Pouwels
Iris D. Kilsdonk

Background and Purpose In vivoidentification of white matter lesions plays a key-role in evaluation of patients with multiple sclerosis (MS). Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking. In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset. Methods 70 MS patients (median EDSS of 2. 0 [range 0. 0–6. 5]) were included from a six-center dataset of the MAGNIMS Study Group (www. magnims. eu) which included 2D FLAIR and 3D T1 images with manual lesion segmentation as a reference. Automated lesion segmentations were produced using five algorithms: Cascade; Lesion Segmentation Toolbox (LST) with both the Lesion growth algorithm (LGA) and the Lesion prediction algorithm (LPA); Lesion-Topology preserving Anatomical Segmentation (Lesion-TOADS); and k-Nearest Neighbor with Tissue Type Priors (kNN-TTP). Main software parameters were optimized using a training set (N = 18), and formal testing was performed on the remaining patients (N = 52). To evaluate volumetric agreement with the reference segmentations, intraclass correlation coefficient (ICC) as well as mean difference in lesion volumes between the automated and reference segmentations were calculated. The Similarity Index (SI), False Positive (FP) volumes and False Negative (FN) volumes were used to examine spatial agreement. All analyses were repeated using a leave-one-center-out design to exclude the center of interest from the training phase to evaluate the performance of the method on ‘unseen’ center. Results Compared to the reference mean lesion volume (4. 85 ± 7. 29 mL), the methods displayed a mean difference of 1. 60 ± 4. 83 (Cascade), 2. 31 ± 7. 66 (LGA), 0. 44 ± 4. 68 (LPA), 1. 76 ± 4. 17 (Lesion-TOADS) and −1. 39 ± 4. 10 mL (kNN-TTP). The ICCs were 0. 755, 0. 713, 0. 851, 0. 806 and 0. 723, respectively. Spatial agreement with reference segmentations was higher for LPA (SI = 0. 37 ± 0. 23), Lesion-TOADS (SI = 0. 35 ± 0. 18) and kNN-TTP (SI = 0. 44 ± 0. 14) than for Cascade (SI = 0. 26 ± 0. 17) or LGA (SI = 0. 31 ± 0. 23). All methods showed highly similar results when used on data from a center not used in software parameter optimization. Conclusion The performance of the methods in this multi-center MS dataset was moderate, but appeared to be robust even with new datasets from centers not included in training the automated methods.

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