Segmentation of meristem cells by an automated opinion algorithm

Meristem cells are irregularly shaped and appear in confocal images as dark areas surrounded by bright ones. Images are characterized by regions of very low contrast and absolute loss of edges deeper into the meristem. Edges are blurred, discontinuous, sometimes indistinguishable, and the intensity level inside the cells is similar to the background of the image. Recently, a technique called Parametric Segmentation Tuning was introduced for the optimization of segmentation parameters in diatom images. This paper presents a PST-tuned automatic segmentation method of meristem cells in microscopy images based on mathematical morphology. The optimal parameters of the algorithm are found by means of an iterative process that compares the segmented images obtained by successive variations of the parameters. Then, an optimization function is used to determine which pair of successive images allows for the best segmentation. The technique was validated by comparing its results with those obtained by a level set algorithm and a balloon segmentation technique. The outcomes show that our methodology offers better results than two free available state-of-the-art alternatives, being superior in all cases studied, losing 9.09% of the cells in the worst situation, against 75.81 and 25.45 obtained in the level set and the balloon segmentation techniques, respectively. The optimization method can be employed to tune the parameters of other meristem segmentation methods

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

3D time series analysis of cell shape using Laplacian approaches

Background: Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes. Results: We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells. Conclusions: The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations

Quantitation of Cellular Dynamics in Growing Arabidopsis Roots with Light Sheet Microscopy

To understand dynamic developmental processes, living tissues must be imaged frequently and for extended periods of time. Root development is extensively studied at cellular resolution to understand basic mechanisms underlying pattern formation and maintenance in plants. Unfortunately, ensuring continuous specimen access, while preserving physiological conditions and preventing photo-damage, poses major barriers to measurements of cellular dynamics in indeterminately growing organs such as plant roots. We present a system that integrates optical sectioning through light sheet fluorescence microscopy with hydroponic culture that enables us to image at cellular resolution a vertically growing Arabidopsis root every few minutes and for several consecutive days. We describe novel automated routines to track the root tip as it grows, track cellular nuclei and identify cell divisions. We demonstrate the system's capabilities by collecting data on divisions and nuclear dynamics.Comment: * The first two authors contributed equally to this wor

Long-Term Quantitative Microscopy: From Microbial Population Dynamics to Growth of Plant Roots

Quantitative optical measurements at the micron scale have been crucial to the study of multiple biological processes, including bacterial chemotaxis, eukaryotic gene expression and y development. Extending measurements to long time scales allows complete observation of processes that are otherwise studied piecemeal, such as development and evolution. This thesis describes the development of two types of microscope for making long term, quantitative measurements, and the tools for image analysis. The rst device is a digital holographic microscope for measuring microbial population dynamics. It allows three dimensional localization of hundreds of cells within a mm3 sized volume, at micron resolution and an acquisition period of minutes. The technique is simple and inexpensive, which enabled us to construct ten replicate devices for parallel measurements. Each device incorporates precise and programmable control of light and temperature for the microbial ecosystem. Experiments were performed with the green algae Chlamydomonas reinhardtii and the ciliate Tetrahymena reinhardtii, both together and in isolation, and continued for as long as 90 days. The population dynamics exhibited a striking degree of repeatability, despite the presence of added noise in the illumination, spatial gradients of cell density, convection currents and phenotypic changes of both species. The second device is a thin light sheet fluorescence microscope for tracking nuclei in growing roots of the flowering plant Arabidopsis thaliana. The device incorporates a chamber designed to maintain optical quality while providing conditions for root growth. Optical feedback to a translation stage is used to maintain the root tip in the fi eld of view as the root grows by centimeters over several days. Data from a three day experiment is presented to demonstrate the technique. Over 1,000 nuclei were tracked simultaneously, and hundreds of cell divisions were automatically identif ed. The device was also used to image the regeneration of a root tip after surgical excision. The data corroborate earlier investigations at a more detailed level than was previously possible

Precision Automation of Cell Type Classification and Sub-Cellular Fluorescence Quantification from Laser Scanning Confocal Images

While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to 1) segment radial plant organs into individual cells, 2) classify cells into cell type categories based upon random forest classification, 3) divide each cell into sub-regions, and 4) quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types

Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions

Developing 3D SEM in a broad biological context

When electron microscopy (EM) was introduced in the 1930s it gave scientists their first look into the nanoworld of cells. Over the last 80 years EM has vastly increased our understanding of the complex cellular structures that underlie the diverse functions that cells need to maintain life. One drawback that has been difficult to overcome was the inherent lack of volume information, mainly due to the limit on the thickness of sections that could be viewed in a transmission electron microscope (TEM). For many years scientists struggled to achieve three-dimensional (3D) EM using serial section reconstructions, TEM tomography, and scanning EM (SEM) techniques such as freeze-fracture. Although each technique yielded some special information, they required a significant amount of time and specialist expertise to obtain even a very small 3D EM dataset. Almost 20 years ago scientists began to exploit SEMs to image blocks of embedded tissues and perform serial sectioning of these tissues inside the SEM chamber. Using first focused ion beams (FIB) and subsequently robotic ultramicrotomes (serial block-face, SBF-SEM) microscopists were able to collect large volumes of 3D EM information at resolutions that could address many important biological questions, and do so in an efficient manner. We present here some examples of 3D EM taken from the many diverse specimens that have been imaged in our core facility. We propose that the next major step forward will be to efficiently correlate functional information obtained using light microscopy (LM) with 3D EM datasets to more completely investigate the important links between cell structures and their functions. Lay Description Life happens in three dimensions. For many years, first light, and then EM struggled to image the smallest parts of cells in 3D. With recent advances in technology and corresponding improvements in computing, scientists can now see the 3D world of the cell at the nanoscale. In this paper we present the results of high resolution 3D imaging in a number of diverse cells and tissues from multiple species. 3D reconstructions of cell structures often revealed them to be significantly more complex when compared to extrapolations made from 2D studies. Correlating functional 3D LM studies with 3D EM results opens up the possibility of making new strides in our understanding of how cell structure is connected to cell function