Improved Segmentation of Mouse MRI Data using Multiple Automatically Generated Templates

Mallar Chakravarty^1,2, Matthijs Christiaan van Eede¹, Jason P. Lerch¹

In human MRI experiments, segmentation of neuroanatomy is often accomplished using a single atlas based nonlinear transformation estimation. The accuracy of this technique is limited by errors in the nonlinear transformation estimated, differences in the neuroanatomy between the template brain and the subject, or label resampling errors. Recent work demonstrates improvement of these segmentation techniques through the use of a manually generated template library. In this methodology, instead of using a single expertly labeled MRI template, a number of different templates are manually labeled, and transformations are estimated to match a single subject to each of these templates. After the nonlinear transformations are applied to the anatomical labels, a histogram of labels generated at each voxel can be used to inform the final segmentation on a voxel-by-voxel basis. This template library approach thus improves segmentation accuracy by accounting for varying anatomy through the use of different templates and compensating for registration algorithm inaccuracy by virtue of the multiple registrations needed from each MRI in the template to the target. In the segmentation of MRI data from inbred laboratory mice strains, however, the confounds of variable neuroanatomy are limited, and segmentation errors therefore result from registration inaccuracy and resampling errors. We hypothesize that segmentations can be improved if resampling and nonlinear transformation errors are reduced. Here, we test this hypothesis by implementing a multi-atlas segmentation scheme using automatically generated atlases (instead of manually labeled ones) and verified the accuracy of the segmentation using manually derived gold standards of the neuroanatomy.