We present an algorithm for passing from dense noisy neuroanatomical segmentations, directly to a complexity-reduced representation with respect to a deformed smooth template surface, bypassing the need for triangulation of any target data. We demonstrate the utility of this algorithm toward improving reproducibility of hippocampal definitions, using a dataset containing 4 MR images per subject, two within the same visit on each of two dates, with dense segmentations provided by unedited longitudinal Freesurfer analysis. We quantify reproducibility of intra-visit and inter-visit variability through L2 distances and Hausdorff distances between pairs of segmentations, and show that our method results in a statistically significant improvement by a factor of 1.63 to more than 3-fold.
CITATION STYLE
Tward, D., Jovicich, J., Soricelli, A., Frisoni, G., Trouvé, A., Younes, L., & Miller, M. (2014). Improved reproducibility of neuroanatomical definitions through diffeomorphometry and complexity reduction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8679, 223–230. https://doi.org/10.1007/978-3-319-10581-9_28
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