A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria

ColabSeg: An interactive tool for editing, processing, and visualizing membrane segmentations from cryo-ET data

Cellular cryo-electron tomography (cryo-ET) has emerged as a key method to unravel the spatial and structural complexity of cells in their near-native state at unprecedented molecular resolution. To enable quantitative analysis of the complex shapes and morphologies of lipid membranes, the noisy three-dimensional (3D) volumes must be segmented. Despite recent advances, this task often requires considerable user intervention to curate the resulting segmentations. Here, we present ColabSeg, a Python-based tool for processing, visualizing, editing, and fitting membrane segmentations from cryo-ET data for downstream analysis. ColabSeg makes many well-established algorithms for point-cloud processing easily available to the broad community of structural biologists for applications in cryo-ET through its graphical user interface (GUI). We demonstrate the usefulness of the tool with a range of use cases and biological examples. Finally, for a large Mycoplasma pneumoniae dataset of 50 tomograms, we show how ColabSeg enables high-throughput membrane segmentation, which can be used as valuable training data for fully automated convolutional neural network (CNN)-based segmentation.

Semiautomatic contour tracking method for biological object segmentation in thin-section electron microscope images with modified zero DC component-type Gabor wavelets

In electron microscopic image processing, artificial intelligence (AI) is a powerful method for segmentation. Because creating training data remains time-consuming and burdensome, a simple and accurate segmentation tool, which is effective and does not rely on manual drawings, is necessary to create training data for AI and to support immediate image analysis. A Gabor wavelet-based contour tracking method has been devised as a step toward realizing such a tool. Although many papers on Gabor filter-based and Gabor filter bank-based texture segmentations have been published, previous studies did not apply the Gabor wavelet-based method to straightforwardly detect membrane-like ridges and step edges for segmentation because earlier works used a nonzero DC component-type Gabor wavelets. The DC component has a serious flaw in such detection. Although the DC component can be removed by a formula that satisfies the wavelet theory or by a log-Gabor function, this is not practical for the proposed scheme. Herein, we devised modified zero DC component-type Gabor wavelets. The proposed method can practically confine a wavelet within a small image area. This type of Gabor wavelet can appropriately track various contours of organelles appearing in thin-section transmission electron microscope images prepared by the freeze-substitution fixation method. The proposed method not only more accurately tracks ridge and step edge contours but also tracks pattern boundary contours consisting of slightly different image patterns. Simulations verified these results.

A Close Look at Molecular Self-Assembly with the Transmission Electron Microscope

Molecular self-assembly is pervasive in the formation of living and synthetic materials. Knowledge gained from research into the principles of molecular self-assembly drives innovation in the biological, chemical, and materials sciences. Self-assembly processes span a wide range of temporal and spatial domains and are often unintuitive and complex. Studying such complex processes requires an arsenal of analytical and computational tools. Within this arsenal, the transmission electron microscope stands out for its unique ability to visualize and quantify self-assembly structures and processes. This review describes the contribution that the transmission electron microscope has made to the field of molecular self-assembly. An emphasis is placed on which TEM methods are applicable to different structures and processes and how TEM can be used in combination with other experimental or computational methods. Finally, we provide an outlook on the current challenges to, and opportunities for, increasing the impact that the transmission electron microscope can have on molecular self-assembly.

Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network

Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscopy (EM) images have proven to be a difficult and challenging task. This paper puts forward an effective and automated pipeline based on deep learning to realize mitochondria segmentation in different EM images. The proposed pipeline consists of three parts: (1) utilizing image registration and histogram equalization as image pre-processing steps to maintain the consistency of the dataset; (2) proposing an effective approach for 3D mitochondria segmentation based on a volumetric, residual convolutional and deeply supervised network; and (3) employing a 3D connection method to obtain the relationship of mitochondria and displaying the 3D reconstruction results. To our knowledge, we are the first researchers to utilize a 3D fully residual convolutional network with a deeply supervised strategy to improve the accuracy of mitochondria segmentation. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of our method, and the Jaccard index of our segmentation (91.8% in anisotropy, 90.0% in isotropy) and F1 score of detection (92.2% in anisotropy, 90.9% in isotropy) suggest that our approach achieved state-of-the-art results. Our fully automated pipeline contributes to the development of neuroscience by providing neurologists with a rapid approach for obtaining rich mitochondria statistics and helping them elucidate the mechanism and function of mitochondria.

An automated pipeline for mitochondrial segmentation on ATUM-SEM stacks

It is possible now to look more closely into mitochondrial physical structures due to the rapid development of electron microscope (EM). Mitochondrial physical structures play important roles in both cellular physiology and neuronal functions. Unfortunately, the segmentation of mitochondria from EM images has proven to be a difficult and challenging task, due to the presence of various subcellular structures, as well as image distortions in the sophisticated background. Although the current state-of-the-art algorithms have achieved some promising results, they have demonstrated poor performances on these mitochondria which are in close proximity to vesicles or various membranes. In order to overcome these limitations, this study proposes explicitly modelling the mitochondrial double membrane structures, and acquiring the image edges by way of ridge detection rather than by image gradient. In addition, this study also utilizes group-similarity in context to further optimize the local misleading segmentation. Then, the experimental results determined from the images acquired by automated tape-collecting ultramicrotome scanning electron microscopy (ATUM-SEM) demonstrate the effectiveness of this study's proposed algorithm.

Hierarchical detection and analysis of macromolecular complexes in cryo-electron tomograms using Pyto software

Molecular complexes, arguably the basic units carrying cellular function, can be visualized directly in their native environment by cryo-electron tomography. Here we describe a procedure for the detection of small, pleomorphic membrane-bound molecular complexes in cryo-tomograms by a hierarchical connectivity segmentation. Validation on phantom and real data showed above 90% true positive rates. This segmentation procedure is implemented in the Pyto software package, together with methods for quantitative characterization and classification of complexes detected by our segmentation procedure and for statistical analysis between experimental conditions. Therefore, the methods presented provide a means for the detection and quantitative interpretation of structures captured in cryo-electron tomograms, as well as for the elucidation of their cellular function.

Multiplexed high-content analysis of mitochondrial morphofunction using live-cell microscopy

Mitochondria have a central role in cellular (patho)physiology, and they display a highly variable morphology that is probably coupled to their functional state. Here we present a protocol that allows unbiased and automated quantification of mitochondrial 'morphofunction' (i.e., morphology and membrane potential), cellular parameters (size, confluence) and nuclear parameters (number, morphology) in intact living primary human skin fibroblasts (PHSFs). Cells are cultured in 96-well plates and stained with tetramethyl rhodamine methyl ester (TMRM), calcein-AM (acetoxy-methyl ester) and Hoechst 33258. Next, multispectral fluorescence images are acquired using automated microscopy and processed to extract 44 descriptors. Subsequently, the descriptor data are subjected to a quality control (QC) algorithm based upon principal component analysis (PCA) and interpreted using univariate, bivariate and multivariate analysis. The protocol requires a time investment of ∼4 h distributed over 2 d. Although it is specifically developed for PHSFs, which are widely used in preclinical research, the protocol is portable to other cell types and can be scaled up for implementation in high-content screening.