An improved automated zebrafish larva high-throughput imaging system. (September 2021)

• Record Type: Journal Article
• Authors: Zhang, Gefei; Yu, Xinghu; Huang, Gang; Lei, Dongxu; Tong, Mingsi
• Is Part Of: Computers in biology and medicine. Volume 136(2021)
• Publication Date: 2021-09
• Subjects: Zebrafish larva -- High-throughput imaging -- Machine vision -- Micromanipulation; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104702
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: As a typical multicellular model organism, the zebrafish has been increasingly used in biological research. Despite the efforts to develop automated zebrafish larva imaging systems, existing ones are still defective in terms of reliability and automation. This paper presents an improved zebrafish larva high-throughput imaging system, which makes improvements to the existing designs in the following aspects. Firstly, a single larva extraction strategy is developed to make larva loading more reliable. The aggregated larvae are identified, classified by their numbers and patterns, and separated by the aspiration pipette or water stream. Secondly, the dynamic model of larva motion in the capillary is established and an adaptive robust controller is designed for decelerating the fast-moving larva to ensure the survival rate. Thirdly, rotating the larva to the desired orientation is automated by developing an algorithm to estimate the larva's initial rotation angle. For validating the improved larva imaging system, a real-time heart rate monitoring experiment is conducted as an application example. Experimental results demonstrate that the goals of the improvements have been achieved. With these improvements, the improved zebrafish larva imaging system remarkably reduces human intervention and increases the efficiency and success/survival rates of larva imaging. Highlights: We present an improved zebrafish larva high-throughput imaging system, which makes improvements to the existing designs. A single larva extraction strategy is developed to make automated larva loading more reliable. An adaptive robust controller is designed for decelerating the larva to ensure the survival rate. Rotating the larva around the body axis is automated by developing an algorithm to estimate its initial rotation angle. A real-time heart rate monitoring experiment is conducted as an application example to validate the proposed system.