Quantitative characterization of zebrafish development based on multiple classifications using Mueller matrix OCT

Organ development analysis plays an important role in assessing an individual' s growth health. In this study, we present a non-invasive method for the quantitative characterization of zebrafish multiple organs during their growth, utilizing Mueller matrix optical coherence tomography (Mueller matrix OCT) in combination with deep learning. Firstly, Mueller matrix OCT was employed to acquire 3D images of zebrafish during development. Subsequently, a deep learning based U-Net network was applied to segment various anatomical structures, including the body, eyes, spine, yolk sac, and swim bladder of the zebrafish. Following segmentation, the volume of each organ was calculated. Finally, the development and proportional trends of zebrafish embryos and organs from day 1 to day 19 were quantitatively analyzed. The obtained quantitative results revealed that the volume development of the fish body and individual organs exhibited a steady growth trend. Additionally, smaller organs, such as the spine and swim bladder, were successfully quantified during the growth process. Our findings demonstrate that the combination of Mueller matrix OCT and deep learning effectively quantify the development of various organs throughout zebrafish embryonic development. This approach offers a more intuitive and efficient monitoring method for clinical medicine and developmental biology studies.

Automated summary

In this study, a non-invasive method utilizing Mueller matrix optical coherence tomography (Mueller matrix OCT) and deep learning is presented for quantitative characterization of zebrafish organ development. 3D images of zebrafish were obtained using Mueller matrix OCT, and a deep learning based U-Net network was applied for organ segmentation and volume calculation. The results revealed steady growth trends in the volume development of zebrafish body and individual organs, with successful quantification of smaller organs such as the spine and swim bladder. This approach offers a more intuitive and efficient monitoring method for clinical medicine and developmental biology studies.