Robust Landmark Detection for Alignment of Mouse Brain Section Images
This work addresses the need for semi-automated alignment in mouse brain research to facilitate digital atlas development, though it appears incremental as it builds on existing registration frameworks.
The paper tackles the problem of automating landmark detection for aligning mouse brain section images, presenting an unsupervised texture-based method that robustly identifies and matches landmarks corresponding to brain structures under distortion.
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.