In an eLife publication, researchers at Heidelberg University unveiled a convolutional neural network, PlantSeg, that is capable of robust segmentation of plant cells.
The publication featured live images of Arabidopsis Thaliana roots (5 days post germination) that were acquired using the Luxendo MuVi SPIM microscope. The two-sided illumination capability of the MuVi SPIM was used, allow for image acquisition without the need for rotation. The different views of the image were subsequently fused using the Luxendo image processing tool. The low photo-toxicity of the imaging also allowed for long term imaging that allowed the researchers to image the development of the root over the course of a day.
Wolny, A., Cerrone, L., Vijayan, A. et. al., 2020. Accurate and versatile 3D segmentation of plant tissues at cellular resolution. eLife, 9. https://doi.org/10.7554/elife.57613
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.