Work

RegBoost: Enhancing mouse brain image registration using geometric priors and Laplacian interpolation

Year: 2024

Type: article

Abstract: We show in this work that incorporating geometric features and geometry processing algorithms for mouse brain image registration broadens the applicability of registration algorithms and improves the registration accuracy of existing methods. We introduce the preprocessing and postprocessing steps in our proposed framework as RegBoost. We develop a method to align the axis of 3D image stacks by detecting the central planes that pass symmetrically through the image volumes. We then find geometric contours by defining external and internal structures to facilitate image correspondences. We establish Dirichlet boundary conditions at these correspondences and find the displacement map throughout the volume using Laplacian interpolation. We discuss the challenges in our standalone framework and demonstrate how our new approaches can improve the results of existing image registration methods. We expect our new approach and algorithms will have critical applications in brain mapping projects. • Geometric features for registration improve upon existing registration results. • Using 3D over 2D operators ensures better results and continuities across 3D data. • Fully automated registration framework aligns input data to an annotated atlas.

Source: NeuroImage

DOI: 10.1016/j.neuroimage.2024.120981

Authors: Atchuth Naveen Chilaparasetti, Andy Thai, Pan Gao, Xiangmin Xu, M. Gopi

Institution: University of California, Irvine

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,450

Funders: National Institutes of Health, National Institute on Aging