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
Accepted author manuscript
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Boschetto, D, Di Claudio, G, Mirzaei, H, Leong, R and Grisan, E (2016). Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy. Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. San Diego, United States 27 Feb - 03 Mar 2016 SPIE. https://doi.org/10.1117/12.2217183
Automatic classification of endoscopic images for premalignant conditions of the esophagus
Boschetto, D, Gambaretto, G and Grisan, E (2016). Automatic classification of endoscopic images for premalignant conditions of the esophagus. Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. San Diego, United States 27 Feb - 03 Mar 2016 https://doi.org/10.1117/12.2216826
Superpixel-based automatic segmentation of villi in confocal endomicroscopy
Boschetto, D, Mirzaei, H, Leong, RWL and Grisan, E (2016). Superpixel-based automatic segmentation of villi in confocal endomicroscopy. 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). Las Vegas, NV, USA 24 - 27 Feb 2016 pp. 168-171 https://doi.org/10.1109/BHI.2016.7455861
Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis
Cerrolaza, J.J., Grisan, E., Safdar, N., Myers, E., Jago, J., Peters, C.A. and Linguraru, M.G. (2015). Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/embc.2015.7318324