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Fabian Wenzel

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3 papers

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YNIMG Journal 2024 Journal Article

Shape-constrained deformable brain segmentation: Methods and quantitative validation

Lyubomir Zagorchev
Damon E. Hyde
Chen Li
Fabian Wenzel
Nick Fläschner
Arne Ewald
Stefani O’Donoghue
Kelli Hancock

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.

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YNIMG Journal 2013 Journal Article

Voxel-based classification of FDG PET in dementia using inter-scanner normalization

Frank Thiele
Stewart Young
Ralph Buchert
Fabian Wenzel

Statistical mapping of FDG PET brain images has become a common tool in differential diagnosis of patients with dementia. We present a voxel-based classification system of neurodegenerative dementias based on partial least squares (PLS). Such a classifier relies on image databases of normal controls and dementia cases as training data. Variations in PET image characteristics can be expected between databases, for example due to differences in instrumentation, patient preparation, and image reconstruction. This study evaluates (i) the impact of databases from different scanners on classification accuracy and (ii) a method to improve inter-scanner classification. Brain FDG PET databases from three scanners (A, B, C) at two clinical sites were evaluated. Diagnostic categories included normal controls (NC, nA =26, nB =20, nC =24 for each scanner respectively), Alzheimer's disease (AD, nA =44, nB =11, nC =16), and frontotemporal dementia (FTD, nA =13, nB =13, nC =5). Spatially normalized images were classified as NC, AD, or FTD using partial least squares. Supervised learning was employed to determine classifier parameters, whereby available data is sub-divided into training and test sets. Four different database setups were evaluated: (i) “in-scanner”: training and test data from the same scanner, (ii) “x-scanner”: training and test data from different scanners, (iii) “train other”: train on both x-scanners, and (iv) “train all”: train on all scanners. In order to moderate the impact of inter-scanner variations on image evaluation, voxel-by-voxel scaling was applied based on “ratio images”. Good classification accuracy of on average 94% was achieved for the in-scanner setups. Accuracy deteriorated for setups with mismatched scanners (79–91%). Ratio-image normalization improved all results with mismatched scanners (85–92%). In conclusion, automatic classification of individual FDG PET in differential diagnosis of dementia is feasible. Accuracy can vary with respect to scanner or acquisition characteristics of the training image data. The adopted approach of ratio-image normalization has been demonstrated to effectively moderate these effects.

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YNIMG Journal 2010 Journal Article

B-spline-based stereotactical normalization of brain FDG PET scans in suspected neurodegenerative disease: Impact on voxel-based statistical single-subject analysis

Fabian Wenzel
Stewart Young
Florian Wilke
Ivayla Apostolova
Sönke Arlt
Holger Jahn
Frank Thiele
Ralph Buchert

A b-spline-based method ‘Lobster’, originally designed as a general technique for non-linear image registration, was tailored for stereotactical normalization of brain FDG PET scans. Lobster was compared with the normalization methods of SPM2 and Neurostat with respect to the impact on the accuracy of voxel-based statistical analysis. (i) Computer simulation: Seven representative patterns of cortical hypometabolism served as artificial ground truth. They were inserted into 26 normal control scans with different simulated severity levels. After stereotactical normalization and voxel-based testing, statistical maps were compared voxel-by-voxel with the ground truth. This was done at different levels of statistical significance. There was a highly significant effect of the stereotactical normalization method on the area under the resulting ROC curve. Lobster showed the best average performance and was most stable with respect to variation of the severity level. (ii) Clinical evaluation: Statistical maps were obtained for the normal controls as well as patients with Alzheimer's disease (AD, n =44), Lewy-Body disease (LBD, 9), fronto-temporal dementia (FTD, 13), and cortico-basal dementia (CBD, 4). These maps were classified as normal, AD, LBD, FTD, or CBD by two experienced readers. The stereotactical normalization method had no significant effect on classification by of each of the experts, but it appeared to affect agreement between the experts. In conclusion, Lobster is appropriate for use in single-subject analysis of brain FDG PET scans in suspected dementia, both in early diagnosis (mild hypometabolism) and in differential diagnosis in advanced disease stages (moderate to severe hypometabolism). The computer simulation framework developed in the present study appears appropriate for quantitative evaluation of the impact of the different processing steps and their interaction on the performance of voxel-based single-subject analysis.

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