An Adaptive Registration Algorithm for Zebrafish Larval Brain Images

Journal article

Deb, S, Tiso, N, Grisan, E. and Chowdhury, A.S. (2022). An Adaptive Registration Algorithm for Zebrafish Larval Brain Images. Computer methods and programs in biomedicine. 216, p. 106658. https://doi.org/10.1016/j.cmpb.2022.106658
AuthorsDeb, S, Tiso, N, Grisan, E. and Chowdhury, A.S.
Abstract

Background and Objective
Zebrafish (Danio rerio) in their larval stages have grown increasingly popular as excellent vertebrate models for neurobiological research. Researchers can apply various tools in order to decode the neural structure patterns which can aid the understanding of vertebrate brain development. In order to do so, it is essential to map the gene expression patterns to an anatomical reference precisely. However, high accuracy in sample registration is sometimes difficult to achieve due to laboratory- or protocol-dependent variabilities.

Methods
In this paper, we propose an accurate adaptive registration algorithm for volumetric zebrafish larval image datasets using a synergistic combination of attractive Free-Form-Deformation (FFD) and diffusive Demons algorithms.
A coarse registration is achieved first for 3D volumetric data using a 3D affine transformation. A localized registration algorithm in form of a B-splines based FFD is applied next on the coarsely registered volume. Finally, the Demons algorithm is applied on this FFD registered volume for achieving fine registration by making the solution noise resilient.

Results
Experimental procedures are carried out on a number of 72 hpf (hours post fertilization) 3D confocal zebrafish larval datasets. Comparisons with state-of-the-art methods including some ablation studies clearly demonstrate the effectiveness of the proposed method.

Conclusion
Our adaptive registration algorithm significantly aids zebrafish imaging analysis over current methods for gene expression anatomical mapping, such as Vibe-Z. We believe the proposed solution would be able to overcome the requirement
of high quality images which currently limits the applicability of zebrafish in neuroimaging research

KeywordsZebrafish; Zebrafish imaging; Adaptive registration; FFD -Demons synergism
Year2022
JournalComputer methods and programs in biomedicine
Journal citation216, p. 106658
PublisherElsevier
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cmpb.2022.106658
Web address (URL)https://www.sciencedirect.com/science/article/abs/pii/S0169260722000438
Publication dates
Print24 Jan 2022
Publication process dates
Accepted22 Jan 2022
Deposited14 Feb 2022
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