Automated morphometrics on microscopy images of Atlantic cod larvae using Mask R-CNN and classical machine vision techniques

Abstract

Measurements of morphometrical parameters on i.e., fish larvae are useful for assessing the quality and condition of the specimen in environmental research or optimal growth in the cultivation industry. Manually acquiring morphometrical parameters from microscopy images can be time consuming and tedious, this can be a limiting factor when acquiring samples for an experiment. Mask R-CNN, an instance segmentation neural network architecture, has been implemented for finding outlines on parts of interest on fish larvae (Atlantic cod, Gadus morhua). Using classical machine vision techniques on the outlines makes it is possible to acquire morphometrics such as area, diameter, length, and height. The combination of these techniques is providing accurate-, consistent-, and high-volume information on the morphometrics of small organisms, making it possible to sample more data for morphometric analysis.•Capabilities to automatically analyse a set of microscopy images in approximately 2-3 seconds per image, with results that have a high degree of accuracy when compared to morphometrics acquired manually by an expert.•Can be implemented on other species of ichthyoplankton or zooplankton and has successfully been tested on ballan wrasse, zebrafish, lumpsucker and calanoid copepods.

Keywords: Artificial neural networks; Ecotoxicity; Instance segmenting; Machine learning; Machine vision; Microscopy; Morphometrics.