Robust Landmark Detection for Alignment of Mouse Brain Section Images

TL;DR

This paper introduces a semi-automated, atlas-guided system for detecting landmarks in mouse brain section images to improve registration and atlas creation, reducing manual effort and increasing robustness.

Contribution

It presents an unsupervised method for landmark detection and matching based on texture modeling, advancing automated brain image registration.

Findings

Landmarks correspond well with brain structures.

Matching is robust under distortion.

The approach facilitates atlas construction.

Abstract

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.