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Brightfield and fluorescent imaging of whole brain sections are funda- mentaltools of research in mouse brain study. As sectioning and imaging become moreefficient, there is an increasing need to automate the post-processing of sec-tions for alignment and three dimensional visualization. There is a furtherneed to facilitate the development of a digital atlas, i.e. a brain-wide mapannotated with cell type and tract tracing data, which would allow theautomatic 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 systemto construct a histology at- las of mouse brainstem that combines atlas-guidedannotation, landmark-based registration and atlas generation in an iterativeframework. We describe an unsu- pervised approach for identifying and matchingregion and boundary landmarks, based on modelling texture. Experiments showthat the detected landmarks corre- spond well with brain structures, andmatching is robust under distortion. These results will serve as the basis forregistration and atlas building.

Robust Landmark Detection for Alignment of Mouse Brain Section Images