Multi-Atlas Library for Eliminating Normalization Failures in Non-Human Primates

Abstract: Current tools for automated skull stripping, normalization, and segmentation of non-human primate (NHP) brain MRI studies typically demonstrate high failure rates. Many of these failures are due to a poor initial estimate for the affine component of the transformation. The purpose of this study is to introduce a multi-atlas approach to overcome these limitations and drive the failure rate to near zero. A library of study-specific templates (SST) spanning three Old World primate species (Macaca fascicularis, M. mulatta, Chlorocebus aethiops) was created using a previously described unbiased automated approach. Several modifications were introduced to the methodology to improve initial affine estimation at the study-specific template level, and at the individual subject level. These involve performing multiple separate normalizations to a multi-atlas library of templates and selecting the best performing template on the basis of a covariance similarity metric. This template was then used as an initialization for the affine component of subsequent skull stripping and normalization procedures. Normalization failure rate for SST generation and individual-subject segmentation on a set of 150 NHP was evaluated on the basis of visual inspection. The previous automated template creation procedure results in excellent skull stripping, segmentation, and atlas labeling across species. Failure rate at the individual-subject level was approximately 1 %, however at the SST generation level it was 17 %. Using the new multi-atlas approach, failure rate was further reduced to zero for both SST generation and individual subject processing. We describe a multi-atlas library registration approach for driving normalization failures in NHP to zero. It is straightforward to implement, and can have application to a wide variety of existing tools, as well as in difficult populations including neonates and the elderly. This approach is also an important step towards developing fully automated high-throughput processing pipelines that are critical for future high volume multi-center NHP imaging studies for studies of drug abuse and brain health.