Abstract
A brain template that describes the anatomical layout of an “average” brain is an essential building block of neuroimage analysis pipelines. However, a single template is often not sufficient to fully capture the variability in a heterogeneous population. Brain structures have very different shapes and sizes in different clinical and demographic groups. In this paper, we develop a novel neural network model that captures this morphometric variability. Our model learns to compute an attribute-specific spatial deformation that warps a brain template. We train this model on individual brain MRI segmentations in an end-to-end fashion, allowing for fast inference during testing. We demonstrate the ability of our model to deform a brain template given a wide range of ages, presence of disease and different sexes. Detailed qualitative and quantitative experiments are provided in order to demonstrate the flexibility of our model. Finally, we study the surface of the deformed template’s hippocampus to show how our model can be used for shape analysis. The code is freely available at https://github.com/evanmy/conditional_deformation.
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Divided by the total WM/GM volume of a 20 year old male or female.
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Yu, E.M., Dalca, A.V., Sabuncu, M.R. (2020). Learning Conditional Deformable Shape Templates for Brain Anatomy. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_36
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