Accurate and versatile 3D segmentation of plant tissues at cellular resolution

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.

[1]  Constantin Pape,et al.  Leveraging Domain Knowledge to Improve Microscopy Image Segmentation With Lifted Multicuts , 2019, Front. Comput. Sci..

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[4]  Tamily A Weissman,et al.  Brainbow: New Resources and Emerging Biological Applications for Multicolor Genetic Labeling and Analysis , 2015, Genetics.

[5]  Gerhard Reinelt,et al.  Globally Optimal Image Partitioning by Multicuts , 2011, EMMCVPR.

[6]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[7]  Peter Li,et al.  Flood-Filling Networks , 2016, ArXiv.

[8]  Marina Meila,et al.  Comparing clusterings: an axiomatic view , 2005, ICML.

[9]  K. C. Huang,et al.  Cell size and growth regulation in the Arabidopsis thaliana apical stem cell niche , 2016, Proceedings of the National Academy of Sciences.

[10]  S. Eaton,et al.  Segmentation and Quantitative Analysis of Epithelial Tissues. , 2016, Methods in molecular biology.

[11]  Philipp J. Keller,et al.  Real-Time Three-Dimensional Cell Segmentation in Large-Scale Microscopy Data of Developing Embryos. , 2016, Developmental cell.

[12]  Carolyn G Rasmussen,et al.  An overview of plant division-plane orientation. , 2018, The New phytologist.

[13]  Camille Roth,et al.  Natural Scales in Geographical Patterns , 2017, Scientific Reports.

[14]  M. Meilă.  Comparing clusterings---an information based distance , 2007 .

[15]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[16]  H. Leitte,et al.  Rules and Self-Organizing Properties of Post-embryonic Plant Organ Cell Division Patterns , 2016, Current Biology.

[17]  Camille Couprie,et al.  Power Watershed: A Unifying Graph-Based Optimization Framework , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Luis Ibáñez,et al.  The Design of SimpleITK , 2013, Front. Neuroinform..

[19]  P. Koumoutsakos,et al.  MorphoGraphX: A platform for quantifying morphogenesis in 4D , 2015, eLife.

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[22]  Iasonas Kokkinos,et al.  Pushing the Boundaries of Boundary Detection using Deep Learning , 2015, ICLR 2016.

[23]  Anirban Chakraborty,et al.  Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages , 2014, Front. Neuroinform..

[24]  Nathalie Harder,et al.  A benchmark for comparison of cell tracking algorithms , 2014, Bioinform..

[25]  F. Vuolo,et al.  A Growth-Based Framework for Leaf Shape Development and Diversity , 2019, Cell.

[26]  Zhuowen Tu,et al.  Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Yara E. Sánchez-Corrales,et al.  Spatiotemporal coordination of cell division and growth during organ morphogenesis , 2018, PLoS biology.

[28]  F. Cvrčková,et al.  Plant Cell Morphogenesis , 2014, Methods in Molecular Biology.

[29]  Jan Funke,et al.  A Benchmark for Epithelial Cell Tracking , 2018, ECCV Workshops.

[30]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[31]  S. Scholz,et al.  Protocol for rapid clearing and staining of fixed Arabidopsis ovules for improved imaging by confocal laser scanning microscopy , 2019, Plant Methods.

[32]  Fred A. Hamprecht,et al.  End-To-End Learned Random Walker for Seeded Image Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  C. Gasser,et al.  Ovule Development in Wild-Type Arabidopsis and Two Female-Sterile Mutants. , 1992, The Plant cell.

[34]  G. Malandain,et al.  Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution , 2010, Nature Methods.

[35]  Fred A. Hamprecht,et al.  A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation , 2019, ArXiv.

[36]  Joseph F. Murray,et al.  Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation , 2010, Neural Computation.

[37]  Bjoern Andres,et al.  Analysis and Optimization of Graph Decompositions by Lifted Multicuts , 2015, ICML.

[38]  Ullrich Köthe,et al.  The Mutex Watershed: Efficient, Parameter-Free Image Partitioning , 2018, ECCV.

[39]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[40]  Fred A Hamprecht,et al.  Multicut brings automated neurite segmentation closer to human performance , 2017, Nature Methods.

[41]  Thomas Brox,et al.  U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

[42]  J. Lohmann,et al.  Live imaging of Arabidopsis development. , 2014, Methods in molecular biology.

[43]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[44]  Fred A. Hamprecht,et al.  ilastik: interactive machine learning for (bio)image analysis , 2019, Nature Methods.

[45]  Johannes Stegmaier,et al.  CNN-Based Preprocessing to Optimize Watershed-Based Cell Segmentation in 3D Confocal Microscopy Images , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[46]  Eric O. Postma,et al.  Learning scale-variant and scale-invariant features for deep image classification , 2016, Pattern Recognit..

[47]  Christopher Churas,et al.  CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation , 2018, Nature Methods.

[48]  Euan A. Ashley,et al.  Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments , 2016, PLoS Comput. Biol..

[49]  Chandan Singh,et al.  Large Scale Image Segmentation with Structured Loss Based Deep Learning for Connectome Reconstruction , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Sébastien Ourselin,et al.  Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.

[51]  William Graf,et al.  Deep learning for cellular image analysis , 2019, Nature Methods.

[52]  Eugene W. Myers,et al.  Moral Lineage Tracing , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Chandan Singh,et al.  A Deep Structured Learning Approach Towards Automating Connectome Reconstruction from 3D Electron Micrographs , 2017, ArXiv.

[54]  Jim Haseloff,et al.  High-resolution live imaging of plant growth in near physiological bright conditions using light sheet fluorescence microscopy. , 2011, The Plant journal : for cell and molecular biology.

[55]  Eugene W. Myers,et al.  Efficient Algorithms for Moral Lineage Tracing , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[56]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[57]  H. Nusbaum,et al.  Formation of lateral root meristems is a two-stage process. , 1995, Development.

[58]  H. Sebastian Seung,et al.  Superhuman Accuracy on the SNEMI3D Connectomics Challenge , 2017, ArXiv.

[59]  Kaiming He,et al.  Group Normalization , 2018, ECCV.

[60]  M. Hülskamp,et al.  Wild‐type ovule development in Arabidopsis thaliana: a light microscope study of cleared whole‐mount tissue , 1995 .

[61]  Pascal Fua,et al.  Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features , 2012, IEEE Transactions on Medical Imaging.

[62]  Min Xu,et al.  Learn to segment single cells with deep distance estimator and deep cell detector , 2018, Comput. Biol. Medicine.

[63]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[64]  P. Benfey,et al.  Organization and cell differentiation in lateral roots of Arabidopsis thaliana. , 1997, Development.