Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM

Kwame S. Kutten; Joshua T. Vogelstein; Nicolas Charon; Li Ye; Karl Deisseroth M.D.; Michael I. Miller

doi:10.1117/12.2227444

29 April 2016 Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM

Kwame S. Kutten, Joshua T. Vogelstein, Nicolas Charon, Li Ye, Karl Deisseroth M.D., Michael I. Miller

Proceedings Volume 9896, Optics, Photonics and Digital Technologies for Imaging Applications IV; 989616 (2016) https://doi.org/10.1117/12.2227444
Event: SPIE Photonics Europe, 2016, Brussels, Belgium

Abstract

The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically finds the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images; our code is available as open source software at http://NeuroData.io.

Citation Download Citation

Kwame S. Kutten, Joshua T. Vogelstein, Nicolas Charon, Li Ye, Karl Deisseroth M.D., and Michael I. Miller "Deformably registering and annotating whole CLARITY brains to an atlas via masked LDDMM", Proc. SPIE 9896, Optics, Photonics and Digital Technologies for Imaging Applications IV, 989616 (29 April 2016); https://doi.org/10.1117/12.2227444

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