Brightfield and fluorescent imaging of whole brain sections are funda- mental tools of research in mouse brain study. As sectioning and imaging become more efficient, there is an increasing need to automate the post-processing of sec- tions for alignment and three dimensional visualization. There is a further need to facilitate the development of a digital atlas, i.e. a brain-wide map annotated with cell type and tract tracing data, which would allow the automatic registra- tion of images stacks to a common coordinate system. Currently, registration of slices requires manual identification of landmarks. In this work we describe the first steps in developing a semi-automated system to construct a histology at- las of mouse brainstem that combines atlas-guided annotation, landmark-based registration and atlas generation in an iterative framework. We describe an unsu- pervised approach for identifying and matching region and boundary landmarks, based on modelling texture. Experiments show that the detected landmarks corre- spond well with brain structures, and matching is robust under distortion. These results will serve as the basis for registration and atlas building.
@article{arxiv.1803.03420,
title = {Robust Landmark Detection for Alignment of Mouse Brain Section Images},
author = {Yuncong Chen and David Kleinfeld and Martyn Goulding and Yoav Freund},
journal= {arXiv preprint arXiv:1803.03420},
year = {2018}
}