Phenotyping on microscopic scale using DIC microscopy

Image analysis of Arabidopsis (Arabidopsis thaliana) plants is an important method for studying plant growth. Most work on automated analysis focuses on full rosette analysis, often in a high-throughput monitoring system. In this talk we propose a new workflow that analysis plant growth on a microscopic scale. This approach results in more detail than the common growth measurements, i.e. analysis of the number of cells, the average cell size, etc. The proposed workflow uses differential interference contrast (DIC) microscopy to visualise cells. DIC microscopy is preferred over fluorescence techniques because it provides a very fast methodology (i.e. image analysis is already possible after 1 day) and it also results in clear contrast in the samples. Although these images are easy to interpret by a human operator, they pose several challenges for automated computer vision methods. In our proposed talk we circumvent most of these challenges by combining multiple images, acquired with different microscopy settings. This approach allows us to automatically segment and analyse cells in the images. The proposed workflow enables a new form of automated phenotyping on microscopic scale

Application of Image Analysis for the Identification of Prehistoric Ceramic Production Technologies in the North Caucasus (Russia, Bronze/Iron Age)

The recent advances in microscopy and scanning techniques enabled the image analysis of archaeological objects in a high resolution. From the direct measurements in images, shapes and related parameters of the structural elements of interest can be derived. In this study, image analysis in 2D/3D is applied to archaeological ceramics, in order to obtain clues about the ceramic pastes, firing and shaping techniques. Images were acquired by the polarized light microscope, scanning electron microscopy (SEM) and 3D micro X-ray computed tomography (µ-CT) and segmented using Matlab. 70 ceramic sherds excavated at Ransyrt 1 (Middle-Late Bronze Age) and Kabardinka 2 (late Bronze–early Iron Age), located in in the North Caucasian mountains, Russia, were investigated. The size distribution, circularity and sphericity of sand grains in the ceramics show site specific difference as well as variations within a site. The sphericity, surface area, volume and Euler characteristic of pores show the existence of various pyrometamorphic states between the ceramics and within a ceramic. Using alignments of pores and grains, similar pottery shaping techniques are identified for both sites. These results show that the image analysis of archaeological ceramics can provide detailed information about the prehistoric ceramic production technologies with fast data availability

Quantitative imaging of the collective cell movements shaping an embryo

The recent development of imaging and image processing techniques, such as 4D microscopy and 3D cell tracking, enables analysis through quantification of the movement of large cell populations in vivo. These imaging approaches provide an opportunity to study embryonic morphogenesis during development from the level of cellular processes to the scale of entire organism. Image analysis reveals cell collective behaviors that shape an embryo and offers some surprising insights into the cell-cell interactions involved in concerted movements. We illustrate the power of this approach by studying the early development of Drosophila embryos

Evaluating performance in three-dimensional fluorescence microscopy

In biological fluorescence microscopy, image contrast is often degraded by a high background arising from out of focus regions of the specimen. This background can be greatly reduced or eliminated by several modes of thick specimen microscopy, including techniques such as 3-D deconvolution and confocal. There has been a great deal of interest and some confusion about which of these methods is ‘better’, in principle or in practice. The motivation for the experiments reported here is to establish some rough guidelines for choosing the most appropriate method of microscopy for a given biological specimen. The approach is to compare the efficiency of photon collection, the image contrast and the signal-to-noise ratio achieved by the different methods at equivalent illumination, using a specimen in which the amount of out of focus background is adjustable over the range encountered with biological samples. We compared spot scanning confocal, spinning disk confocal and wide-field/deconvolution (WFD) microscopes and find that the ratio of out of focus background to in-focus signal can be used to predict which method of microscopy will provide the most useful image. We also find that the precision of measurements of net fluorescence yield is very much lower than expected for all modes of microscopy. Our analysis enabled a clear, quantitative delineation of the appropriate use of different imaging modes relative to the ratio of out-of-focus background to in-focus signal, and defines an upper limit to the useful range of the three most common modes of imaging

A comparative study of quantitative methods in ore microscopy: digital image analysis vs. point counter device

Quantitative mineralogical analyses of metallic concentrates from an ore-processing plant with reflected light microscopy have been carried out independently, on the same samples, by an expert mineralogist using a point counter device (PCD), and by digital image analysis (DIA) operated by a post-graduate student in order to compare the performance and results obtained with both methods

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes

Imaging Ferroelectric Domains via Charge Gradient Microscopy Enhanced by Principal Component Analysis

Local domain structures of ferroelectrics have been studied extensively using various modes of scanning probes at the nanoscale, including piezoresponse force microscopy (PFM) and Kelvin probe force microscopy (KPFM), though none of these techniques measure the polarization directly, and the fast formation kinetics of domains and screening charges cannot be captured by these quasi-static measurements. In this study, we used charge gradient microscopy (CGM) to image ferroelectric domains of lithium niobate based on current measured during fast scanning, and applied principal component analysis (PCA) to enhance the signal-to-noise ratio of noisy raw data. We found that the CGM signal increases linearly with the scan speed while decreases with the temperature under power-law, consistent with proposed imaging mechanisms of scraping and refilling of surface charges within domains, and polarization change across domain wall. We then, based on CGM mappings, estimated the spontaneous polarization and the density of surface charges with order of magnitude agreement with literature data. The study demonstrates that PCA is a powerful method in imaging analysis of scanning probe microscopy (SPM), with which quantitative analysis of noisy raw data becomes possible