Building a Data-driven 3D Atlas for Mouse Brainstem

Building a brain atlas is labor-intensive. Traditionally it involves annotating a small number of specimens and therefore fails to capture brain-to-brain variations which are essential for many applications such as surgical planning and projection area identification. We propose an automated atlas building system which produces a probabilistic anatomical model and a set of landmark detectors. The texture-based detectors help register section series to the model, which describes mean shape/position and position variance of major brainstem nuclei and tracts. The model is updateable from registered data. The atlas can potentially serve as a framework for compiling cell type and projection data. (SfN 2016 poster)

Unsupervised Discovery of Robust Landmarks from Brain Section Series

Brightfield and fluorescent imaging of whole brain sections are fundamental tools of research in mouse brain study. As sectioning and imaging becomemore efficient, there is an increasing need to automate the post-processing of sections for alignment and three dimensional visualization. There is a further needto facilitate the development of a digital atlas, i.e. a brain-wide map annotatedwith cell type and tract tracing data, which would allow the automatic registration of images stacks to a common coordinate system. Currently, registration ofslices requires manual identification of landmarks. In this work we describe the first steps in developing a semi-automated system to construct a histology atlas of mouse brainstem that combines atlas-guided annotation, landmark-based registration and atlas generation in an iterative framework. We describe an unsupervised approach for identifying and matching region and boundary landmarks,based on modelling texture. Experiments show that the detected landmarks correspond well with brain structures, and matching is robust under distortion. Theseresults will serve as the basis for registration and atlas building.

Texture Landmark Detection in Mouse Brain Images Using Significance-based Boosting