Abstract
Online atlasing, i.e. incrementing an atlas with new images as they are acquired, is key when performing studies on databases very large or still being gathered. We propose to this end a new diffeomorphic online atlasing method without having to perform again the atlasing process from scratch. New subjects are integrated following an iterative procedure gradually shifting the centroid of the images to its final position, making it computationally cheap to update regularly an atlas as new images are acquired (only needing a number of registrations equal to the number of new subjects). We evaluate this iterative centroid approach through the analysis of the sharpness and variance of the resulting atlases, and the transformations of images, comparing their deviations from a conventional method. We demonstrate that the transformations divergence between the two approaches is small and stable and that both atlases reach equivalent levels of image quality.
The research leading to these results has been supported by the ANR MAIA project, grant ANR-15-CE23-0009 of the French National Research Agency (http://recherche.imt-atlantique.fr/maia) and La Région Bretagne.
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Notes
- 1.
Anima: Open source software for medical image processing from the Empenn team. https://github.com/Inria-Visages/Anima-Public - RRID:SCR\(\_\)017017.
- 2.
Anima-Scripts: Open source scripts using Anima software for medical image processing from the Empenn team. https://github.com/Inria-Visages/Anima-Scripts-Public - RRID:SCR\(\_\)017072.
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Legouhy, A., Commowick, O., Rousseau, F., Barillot, C. (2019). Online Atlasing Using an Iterative Centroid. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_41
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DOI: https://doi.org/10.1007/978-3-030-32248-9_41
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