Abstract
The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain.
Highlights
The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets
We present automated mouse atlas propagation (aMAP), a tool that internally uses and provides a graphical front-end to NiftyReg, that we modified to enable rapid processing of high-resolution 3D light microscopy data. aMAP permits propagation of a 3D mouse atlas of the entire adult mouse brain in 40 min and its accuracy and reliability is shown to be on par with expert human raters
Target structures were presented within six serial two-photon (STP) image stacks (40 coronal planes per stack containing tissue background fluorescence (n 1⁄4 5 brains) or sparse red fluorescent protein (RFP) labelling (n 1⁄4 1 brain)) obtained from adult C57BL/6 mice
Summary
The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. One critical aspect regarding the implementation of such pipelines is ensuring that the quality of the resulting segmentation—previously achieved by expert neuroanatomists relying on their experience and detailed visual inspection of the data—is not compromised Such high-throughput microscopy instrumentation produces large volumes of high-resolution three-dimensional (3D) data and relies on the accuracy of automated segmentation, yet to date there has been only indirect assessment of segmentation quality and no agreement on a standard method of implementation, with individual labs using unpublished in-house tools[2,14,16] or an open source clinical image registration tool (Elastix18) with unpublished parameters[1,17]. We present aMAP, a tool that internally uses and provides a graphical front-end to NiftyReg (a rapid image registration toolkit, originally developed for human MRI data19), that we modified to enable rapid processing of high-resolution 3D light microscopy data. aMAP permits propagation of a 3D mouse atlas of the entire adult mouse brain in 40 min and its accuracy and reliability is shown to be on par with expert human raters
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