Abstract
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
Highlights
Large-scale quantitative study of morphogenesis in a multicellular organism entails an accurate estimation of the shape of all cells across multiple specimen
Our choice of graph partitioning as the second step is inspired by a body of work on segmentation for nanoscale connectomics, where such methods have been shown to outperform more simple post-processing of the boundary maps (Beier et al, 2017; Funke et al, 2019a; Briggman et al, 2009)
Default parameters have been chosen to deliver good results on most type of data, we show that a substantial improvement can be obtained by parameter tuning, in case of the tissue 3D Digital Tissue Atlas tuning improved segmentation by a factor of two as measured with the Adapted Rand error (ARand) error (Table 1, right)
Summary
Large-scale quantitative study of morphogenesis in a multicellular organism entails an accurate estimation of the shape of all cells across multiple specimen. State-of-the-art light microscopes allow for such analysis by capturing the anatomy and development of plants and animals in terabytes of highresolution volumetric images. With such microscopes in routine use, segmentation of the resulting images has become a major bottleneck in the downstream analysis of large-scale imaging experiments. In the early days of computer vision, boundaries were usually found by edge detection algorithms (Canny, 1986). A combination of edge detectors and other image filters was commonly used as input for a machine learning algorithm, trained to detect boundaries (Lucchi et al, 2012). The most powerful boundary detectors are based on Convolutional Neural
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