PUBLICATION
An adaptive registration algorithm for zebrafish larval brain images
- Authors
- Deb, S., Tiso, N., Grisan, E., Chowdhury, A.S.
- ID
- ZDB-PUB-220204-9
- Date
- 2022
- Source
- Computer methods and programs in biomedicine 216: 106658 (Journal)
- Registered Authors
- Tiso, Natascia
- Keywords
- Adaptive registration, FFD-Demons synergism, Zebrafish imaging
- MeSH Terms
-
- Algorithms*
- Animals
- Brain/diagnostic imaging
- Image Processing, Computer-Assisted/methods
- Imaging, Three-Dimensional/methods
- Larva
- Zebrafish*
- PubMed
- 35114462 Full text @ Comput. Methods Programs Biomed.
Citation
Deb, S., Tiso, N., Grisan, E., Chowdhury, A.S. (2022) An adaptive registration algorithm for zebrafish larval brain images. Computer methods and programs in biomedicine. 216:106658.
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 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.
Conclusions 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.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping