An adaptive registration algorithm for zebrafish larval brain images

• Published in: Computer methods and programs in biomedicine Vol. 216; p. 106658
• Main Authors: Deb, Shoureen, Tiso, Natascia, Grisan, Enrico, Chowdhury, Ananda S.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.04.2022
• Subjects: Adaptive registration; Algorithms; Animals; Brain - diagnostic imaging; FFD-Demons synergism; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Larva; Zebrafish; Zebrafish imaging; FFD-Demons synergism; Zebrafish imaging; Adaptive registration
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106658

•Addresses the problem of registration of Zebrafish larval brain images.•Proposes a synergism of attractive Free-Form-Deformation and diffusive Demons.•Solution can handle varying degrees of freedom at different regions of a Zebrafish larva.•Has widespread applications in developmental neurology. 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. 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. 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.