Volumetric Segmentation of Cell Cycle Markers in Confocal Images

Abstract

I. A BSTRACT Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development – a process referred to as plant phenotyping – is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyse the data in images depicting plant growth. In this paper, a deep learning approach is developed to locate fluorescent markers in 3D timeseries microscopy images. The approach is not dependant on marker morphology; only simple 3D point location annotations are required for training. The approach is evaluated on an unseen timeseries comprising several volumes, capturing growth of plants. Results are encouraging, with an average recall of 0.97 and average F-score of 0.78, despite only a very limited number of simple training annotations. In addition, an in-depth analysis of appropriate loss functions is conducted. To accompany [the finally-published] paper we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (Fiji) software. Network models will be released online.

Code and data availability

The paper describes paper-specific assets: a custom Fiji 'Orthogonal Pixels' 4D point annotation plugin (released as a supplemental file) and trained network models stated to be released online, plus the confocal 4D dataset and CSV point annotations. However, no explicit public repository URL or identifier is provided,

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