AtlasMorph: Learning conditional deformable templates for brain MRI.
Authors
Affiliations (4)
Affiliations (4)
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA. Electronic address: [email protected].
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
- Weill Cornell Medicine, Cornell Tech, Cornell University, New York, NY, 10044, USA.
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Abstract
Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commonly used in medical image analysis for population studies and computational anatomy tasks such as registration and segmentation. Because developing a template is a computationally expensive process, relatively few templates are available. As a result, analysis is often conducted with sub-optimal templates that are not truly representative of the study population, especially when there are large variations within this population. We propose a machine learning framework that uses convolutional registration neural networks to efficiently learn a function that outputs templates conditioned on subject-specific attributes, such as age and sex. We also leverage segmentations, when available, to produce anatomical segmentation maps for the resulting templates. The learned network can also be used to register subject images to the templates. We demonstrate our method on a compilation of 3D brain MRI datasets, and show that it can learn high-quality templates that are representative of populations. We find that annotated conditional templates enable better registration than their unlabeled unconditional counterparts, and outperform other templates construction methods.