PT - JOURNAL ARTICLE AU - Adrian Wolny AU - Lorenzo Cerrone AU - Athul Vijayan AU - Rachele Tofanelli AU - Amaya Vilches Barro AU - Marion Louveaux AU - Christian Wenzl AU - Susanne Steigleder AU - Constantin Pape AU - Alberto Bailoni AU - Salva Duran-Nebreda AU - George Bassel AU - Jan U. Lohmann AU - Fred A. Hamprecht AU - Kay Schneitz AU - Alexis Maizel AU - Anna Kreshuk TI - Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution AID - 10.1101/2020.01.17.910562 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.01.17.910562 4099 - http://biorxiv.org/content/early/2020/01/18/2020.01.17.910562.short 4100 - http://biorxiv.org/content/early/2020/01/18/2020.01.17.910562.full AB - 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, and acquisition settings. 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.