Robust individual template pipeline processing for longitudinal MR images. N. Guizard 1 , V.S. Fonov 1 , B. Aubert-Broche 1 , D. García-Lorenzo 1,2 , P. Coupé 3 , S. F. Eskilden 4 , D. L. Collins 1 1 Montreal Neurological Institute, McGill University, Canada
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Robust individual template pipeline processing for longitudinal MR images
N. Guizard1, V.S. Fonov1, B. Aubert-Broche1, D. García-Lorenzo1,2, P. Coupé3, S. F. Eskilden4, D. L. Collins1
1Montreal Neurological Institute, McGill University, Canada
2CENIR - ICM, Pitié Salpétrière, Paris, France
3LaBRI, CNRS (UMR 5800), Bordeaux, France
4Center of Functionally Integrative Neuroscience, Aarhus University, Denmark
Neurodegenerative diseases such as Alzheimer's disease (AD) present subtle anatomical brain changes before the appearance of clinical symptoms.
Large longitudinal brain imaging datasets are now accessible to investigate such structural changes over time and to evaluate their use as biomarkers of prodromal disease.
Manual segmentation is long, tedious and subject to inter-rater variability. To overcome these issues automatic methods have been proposed. However, automatic approaches are often performed in a cross-sectional manner where each visit is analyzed independently.
In order to minimize bias introduced by cross-sectional analysis of longitudinal dataset, we developed a specific pipeline for longitudinal brain image analysis where an individual template is created for each subject and used as a reference within the pipeline.
Fig. 2 is a random selection of five subject-specific non-lineartemplates with their corresponding brain, ventricles and hippocampimasks before propagation to the subject visits.
Fig.1:Longitudinal pipeline. The different steps performed on each subject visits are represented in the left part of the diagram, where the processes in the left small square represents the cross-sectional (CS) part of the pipeline. The subject template creation (linear and non-linear) is represented in the right side of the figure.The patch-based segmentation approach consists in finding the most similar voxels in a given neighbourhood of the T1-w images library and then fuses the corresponding labels to create the segmentation of the different structures
Our pipeline fulfills the requirements for the NIBAD challenge by providing brain, ventricles and hippocampi volumes and atrophies. All the longitudinal data completed the pipeline, including subjects with less conventional head positioning and poor quality acquisitions. If temporal information was provided, more sophisticated longitudinal regularization could improve the results by correcting for physiological, scanner physical variability, motion artifacts.
Fig.2: Individual non-linear template segmentation: Individual non-linear template brain, ventricles and hippocampi segmentation for subjects 188, 192, 193, 230 and 249
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