Active skeleton for bacteria modeling

The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modeling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness, orientation), an improved boundary accuracy in noisy images, and a natural bacteria-centered coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimizing an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at http://fluobactracker.inrialpes.fr.Comment: Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualizationto appear i

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.Comment: Accepted by Journal of Structural Biolog

Applications of Multivariate Statistical Methods and Simulation Libraries to Analysis of Electron Backscatter Diffraction and Transmission Kikuchi Diffraction Datasets

Multivariate statistical methods are widely used throughout the sciences, including microscopy, however, their utilisation for analysis of electron backscatter diffraction (EBSD) data has not been adequately explored. The basic aim of most EBSD analysis is to segment the spatial domain to reveal and quantify the microstructure, and links this to knowledge of the crystallography (eg crystal phase, orientation) within each segmented region. Two analysis strategies have been explored; principal component analysis (PCA) and k-means clustering. The intensity at individual (binned) pixels on the detector were used as the variables defining the multidimensional space in which each pattern in the map generates a single discrete point. PCA analysis alone did not work well but rotating factors to the VARIMAX solution did. K-means clustering also successfully segmented the data but was computational more expensive. The characteristic patterns produced by either VARIMAX or k-means clustering enhance weak patterns, remove pattern overlap, and allow subtle effects from polarity to be distinguished. Combining multivariate statistical analysis (MSA) approaches with template matching to simulation libraries can significantly reduce computational demand as the number of patterns to be matched is drastically reduced. Both template matching and MSA approaches may augment existing analysis methods but will not replace them in the majority of applications.Comment: manuscript submitted after review at Ultramicroscop

Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles

In this study, we proposed and validated a multi-atlas guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain regions of interest (ROIs) from structural magnetic resonance images (MRIs). One major limitation of existing state-of-the-art 3D FCN segmentation models is that they often apply image patches of fixed size throughout training and testing, which may miss some complex tissue appearance patterns of different brain ROIs. To address this limitation, we trained a 3D FCN model for each ROI using patches of adaptive size and embedded outputs of the convolutional layers in the deconvolutional layers to further capture the local and global context patterns. In addition, with an introduction of multi-atlas based guidance in M-FCN, our segmentation was generated by combining the information of images and labels, which is highly robust. To reduce over-fitting of the FCN model on the training data, we adopted an ensemble strategy in the learning procedure. Evaluation was performed on two brain MRI datasets, aiming respectively at segmenting 14 subcortical and ventricular structures and 54 brain ROIs. The segmentation results of the proposed method were compared with those of a state-of-the-art multi-atlas based segmentation method and an existing 3D FCN segmentation model. Our results suggested that the proposed method had a superior segmentation performance

TEMAS: A Flexible Non-AI Algorithm for Metrology of Single-Core and Core-Shell Nanoparticles from TEM Images

An essential application of electron microscopy is to provide feedback to tune the fabrication of nanoparticles (NPs). Real samples tend to follow a size distribution commonly linked to the synthesis process used and in turn to their functional properties. This study presents an algorithm for measuring particle size distributions in electron microscopy images. State-of-the-art methods based on Artificial Intelligence (e.g., Deep Learning) require extensive datasets of labeled images similar to those expected to be analyzed, and extensive supervised re-training is often required for cross-domain application. In contrast, the non-AI algorithm described in this study is accurate and can be quickly set up for measuring new experimental images in different domains. The accuracy of the method is validated quantitatively and comparing graphical and descriptive statistics. Different size distributions are measured on images of platinum and gold nanocatalysts supported on carbon black, amorphous carbon, and titanium dioxide crystals. Also, images of platinum-iron core-shell NPs supported on thin amorphous carbon film are successfully analyzed. The limitation of evaluating different algorithms for NPs metrology is the lack of standards that different researchers can use as ground truth. In order to overcome this limitation, the images and the ground truth measurements presented here are shared as an open dataset. © 2023 The Authors. Particle & Particle Systems Characterization published by Wiley-VCH GmbH