Learning Conditional Deformable Templates with Convolutional Networks
This work addresses the need for efficient and flexible template generation in domains like neuroimaging, where traditional methods are slow and restrictive, offering a practical solution for clinical use.
The paper tackles the problem of computationally expensive and limited template creation in image analysis by developing a learning framework that efficiently builds universal or conditional deformable templates with neural network alignment, demonstrating its utility in neuroimaging and clinical applications.
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for template creation and image alignment to the template have undergone decades of rich technical development. In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute a single template for the entire population of images, or a few templates for specific sub-groups of the data. In this work, we present a probabilistic model and efficient learning strategy that yields either universal or conditional templates, jointly with a neural network that provides efficient alignment of the images to these templates. We demonstrate the usefulness of this method on a variety of domains, with a special focus on neuroimaging. This is particularly useful for clinical applications where a pre-existing template does not exist, or creating a new one with traditional methods can be prohibitively expensive. Our code and atlases are available online as part of the VoxelMorph library at http://voxelmorph.csail.mit.edu.