Microscopy Image Stacks Research Articles

NRTR: Neuron Reconstruction With Transformer From 3D Optical Microscopy Images.

The neuron reconstruction from raw Optical Microscopy (OM) image stacks is the basis of neuroscience. Manual annotation and semi-automatic neuron tracing algorithms are time-consuming and inefficient. Existing deep learning neuron reconstruction methods, although demonstrating exemplary performance, greatly demand complex rule-based components. Therefore, a crucial challenge is designing an end-to-end neuron reconstruction method that makes the overall framework simpler and model training easier. We propose a Neuron Reconstruction Transformer (NRTR) that, discarding the complex rule-based components, views neuron reconstruction as a direct set-prediction problem. To the best of our knowledge, NRTR is the first image-to-set deep learning model for end-to-end neuron reconstruction. The overall pipeline consists of the CNN backbone, Transformer encoder-decoder, and connectivity construction module. NRTR generates a point set representing neuron morphological characteristics for raw neuron images. The relationships among the points are established through connectivity construction. The point set is saved as a standard SWC file. In experiments using the BigNeuron and VISoR-40 datasets, NRTR achieves excellent neuron reconstruction results for comprehensive benchmarks and outperforms competitive baselines. Results of extensive experiments indicate that NRTR is effective at showing that neuron reconstruction is viewed as a set-prediction problem, which makes end-to-end model training available.

Effect of Composition and Freeze-Thaw on the Network Structure, Porosity and Mechanical Properties of Polyvinyl-Alcohol/Chitosan Hydrogels.

We report the synthesis and characterization of poly (vinyl alcohol) (PVA)/Chitosan (CT) cryogels for applications involving the uptake and entrapment of particulate and bacterial colonies. In particular, we systematically investigated the network and pore structures of the gels as a function of CT content and for different freeze-thaw times, combining Small Angle X-Ray Scattering (SAXS), Scanning Electron Microscopy (SEM), and confocal microscopy. The nanoscale analysis obtained from SAXS shows that while the characteristic correlation length of the network is poorly affected by composition and freeze-thaw time, the characteristic size of heterogeneities associated with PVA crystallites decreases with CT content. SEM investigation evidences a transition to a more homogeneous network structure induced by the incorporation of CT that progressively builds a secondary network around the one formed by PVA. A detailed analysis of confocal microscopy image stacks allows to characterize the 3D porosity of the samples, revealing a significantly asymmetric shape of the pores. While the average volume of single pores increases with increasing CT content, the overall porosity remains almost unchanged as a result of the suppression of smaller pores in the PVA network with the progressive incorporation of the more homogeneous CT network. Increasing the freezing time in the FT cycles also results in a decrease of porosity, which can be associated with a growth in the crosslinking of the network due to PVA crystallization. The linear viscoelastic moduli measured by oscillatory rheology show a qualitatively comparable frequency-dependent response in all cases, with a moderate reduction with increasing CT content. This is attributed to changes in the structure of the strands of the PVA network.

3D electron microscopy and volume-based bouton sorting reveal the selectivity of inputs onto geniculate relay cell and interneuron dendrite segments.

The visual signals evoked at the retinal ganglion cells are modified and modulated by various synaptic inputs that impinge on lateral geniculate nucleus cells before they are sent to the cortex. The selectivity of geniculate inputs for clustering or forming microcircuits on discrete dendritic segments of geniculate cell types may provide the structural basis for network properties of the geniculate circuitry and differential signal processing through the parallel pathways of vision. In our study, we aimed to reveal the patterns of input selectivity on morphologically discernable relay cell types and interneurons in the mouse lateral geniculate nucleus. We used two sets of Scanning Blockface Electron Microscopy (SBEM) image stacks and Reconstruct software to manually reconstruct of terminal boutons and dendrite segments. First, using an unbiased terminal sampling (UTS) approach and statistical modeling, we identified the criteria for volume-based sorting of geniculate boutons into their putative origins. Geniculate terminal boutons that were sorted in retinal and non-retinal categories based on previously described mitochondrial morphology, could further be sorted into multiple subpopulations based on their bouton volume distributions. Terminals deemed non-retinal based on the morphological criteria consisted of five distinct subpopulations, including small-sized putative corticothalamic and cholinergic boutons, two medium-sized putative GABAergic inputs, and a large-sized bouton type that contains dark mitochondria. Retinal terminals also consisted of four distinct subpopulations. The cutoff criteria for these subpopulations were then applied to datasets of terminals that synapse on reconstructed dendrite segments of relay cells or interneurons. Using a network analysis approach, we found an almost complete segregation of retinal and cortical terminals on putative X-type cell dendrite segments characterized by grape-like appendages and triads. On these cells, interneuron appendages intermingle with retinal and other medium size terminals to form triads within glomeruli. In contrast, a second, presumed Y-type cell displayed dendrodendritic puncta adherentia and received all terminal types without a selectivity for synapse location; these were not engaged in triads. Furthermore, the contribution of retinal and cortical synapses received by X-, Y- and interneuron dendrites differed such that over 60% of inputs to interneuron dendrites were from the retina, as opposed to 20% and 7% to X- and Y-type cells, respectively. The results underlie differences in network properties of synaptic inputs from distinct origins on geniculate cell types.

Unbiased Quantification of Golgi Scattering and Golgi-Centrosome Association.

The vertebrate Golgi complex is a large dynamic organelle which undergoes morphological changes and fragmentation both as a part of normal physiological dynamics and under disease conditions. The Golgi is known to have a functionally important relationship with the centrosome. The extent of the spatial association between these two organelles varies in a dynamic and regulated manner. It is essential to have a reliable unbiased approach to evaluate Golgi volume, Golgi extension/scattering in the 3D cell space, and spatial association of the Golgi with the centrosome. It is also important that each of these features is evaluated by a simple metric, one measurement per cell, so that the variability and deviations in the cell population can be easily assessed. Here, we present an approach to analyze confocal microscopy image stacks to easily measure Golgi volume, scattering, and association with the centrosome. The approach is based on a custom MATLAB script, provided here as a supplement, and also uses widely available software (ImageJ and/or Imaris). The output of the script is a table with the following parameters: Golgi volume in voxels, Golgi volume in μm3, "Golgi-Golgi" distance (averaged distance between all Golgi voxels), Golgi-centrosome distance (averaged distance between each Golgi voxel and the nearest mother centriole), and centrosome-centrosome distance (for cells with duplicated centrosome, the distance between the mother centrioles). The approach can also be applied to analyze distribution of any fluorescently- labeled structure within a cell and its association with the centrosome or any single point within the cell volume.

Directly Including Data with Alzheimer’s Publications Through Augmented Reality and Web Technologies – Schol‐AR

AbstractBackgroundAlzheimer’s disease research is multi‐disciplinary by nature and based on many forms of data, from MRI scans to microscopic image stacks. Much of this information is difficult to communicate through standard scientific articles and figures, often resulting in incomplete data reporting and suboptimal comprehension for many readers.MethodWe introduce a community‐accessible data layering approach for directly including modern digital information on top of scientific figures, enabling entire datasets to be included with scientific articles. Through this approach readers can easily view data associated with publications through mobile devices or web browsers, averting the need to simplify complex data into 2D static figures.ResultData layering facilitates communication and comprehension of Alzheimer’s research by enabling accessible viewing of complex information directly with scientific articles, posters, and other formats.ConclusionUsing augmented reality and web technologies we can modernize communication of Alzheimer’s research, bridging the gap between the digital basis of modern science and the natural limitations of journal articles.

Effect of konjac glucomannan and soy soluble polysaccharides on the rheological, microstructural and syneretic properties of rennet gels

This study investigated the rheological, microstructural and syneretic properties of rennet gels as influenced by the addition of soy soluble polysaccharides (2.5 g/kg, 5 g/kg) or konjac glucomannan (0.2 g/kg, 0.4 g/kg) to skim milk. The addition of all polysaccharides enhanced rennet coagulation properties, by reducing rennet coagulation time (RCT) and increasing development of gel firmness. There were also interactive effects between polysaccharide concentration and coagulation pH on parameters such as RCT, showing that the dynamics of gelation of such systems can be modulated by changing polysaccharide concentration and pH. Rennet gels containing 0.4 g/kg konjac glucomannan had significantly higher levels of syneresis (p < 0.05) than the other gels, which was linked to low yield stress and strain and a more open and porous microstructure. Quantitative analysis of 3-D confocal laser scanning microscopy image stacks showed that rennet gels containing konjac glucomannan had a significantly larger (p < 0.05) pore size than the other rennet gels. The ability of these polysaccharides to change the structure of rennet gels and their ensuing syneretic processes may provide a platform for development of novel cheese textures appealing to international consumers who may not be traditional cheese consumers.

Quantitative, in situ visualization of intracellular insulin vesicles in pancreatic beta cells

Characterizing relationships between Zn2+, insulin, and insulin vesicles is of vital importance to the study of pancreatic beta cells. However, the precise content of Zn2+ and the specific location of insulin inside insulin vesicles are not clear, which hinders a thorough understanding of the insulin secretion process and diseases caused by blood sugar dysregulation. Here, we demonstrated the colocalization of Zn2+ and insulin in both single extracellular insulin vesicles and pancreatic beta cells by using an X-ray scanning coherent diffraction imaging (ptychography) technique. We also analyzed the elemental Zn2+ and Ca2+ contents of insulin vesicles using electron microscopy and energy dispersive spectroscopy (EDS) mapping. We found that the presence of Zn2+ is an important characteristic that can be used to distinguish insulin vesicles from other types of vesicles in pancreatic beta cells and that the content of Zn2+ is proportional to the size of insulin vesicles. By using dual-energy contrast X-ray microscopy and scanning transmission X-ray microscopy (STXM) image stacks, we observed that insulin accumulates in the off-center position of extracellular insulin vesicles. Furthermore, the spatial distribution of insulin vesicles and their colocalization with other organelles inside pancreatic beta cells were demonstrated using three-dimensional (3D) imaging by combining X-ray ptychography and an equally sloped tomography (EST) algorithm. This study describes a powerful method to univocally describe the location and quantitative analysis of intracellular insulin, which will be of great significance to the study of diabetes and other blood sugar diseases.

Three-dimensional morphological characterization of colorectal pits from label-free microscopy images.

The prognosis of colorectal cancer (CRC), one of the most prevalent pathologies worldwide, is linked to early detection. Kudo's pit pattern classification states morphological pit patterns of the Lieberkühn crypts by analyzing the superficial mucosa, predicting the histology of colorectal lesions. Its use as a highly accurate two-dimensional diagnostic criterion has increased, mostly involving expert endoscopists' judgment. The processing of autofluorescence images could allow the diagnostic, bypassing staining techniques and decreasing the biopsies, resources and times involved in the inspection. That criterion could be extended by data of the pit three-dimensional (3D) morphology. Thus, this work was aimed at obtaining 3D morphological information by quantifying geometrical and shape descriptors through software processing and analysis of widefield autofluorescence microscopy image stacks acquired by fresh colon tissue samples from a murine model of CRC. Statistical analyses included pits from control mice and from the second (2nd), fourth (4th), and eighth (8th) weeks of treatment. Statistically significant differences were found for almost all parameters between the pits from control and from the 4th treated week, stating that the major morphological changes begin after the 2nd week. In particular, pits from control or initial treatment time points were more tubular, straighter and less rough than the ones from later treatment points. Therefore, they may be more associated to normal or non-neoplastic crypt lumens than linked to adenomas or even cancer crypts. These preliminary outcomes could be considered an advance in 3D pit morphology characterization.

Sequential semi-supervised segmentation for serial electron microscopy image with small number of labels

BackgroundSegmentation of electron microscopic continuous section images by deep learning has attracted attention as a technique to reduce the cost of annotation for researchers attempting to make observations using 3D reconstruction methods. However, when the observed samples are rare, or scanning circumstances are unstable, pursuing generalization performance for newly obtained samples is not appropriate. New methodsWe assume a transductive setting that predicts all labels in a dataset from only partially obtained labels while avoiding the pursuit of generalization performance for unknown data. Then, we propose sequential semi-supervised segmentation (4S), which semi-automatically extracts neural regions from electron microscopy image stacks. This method focuses on the fact that adjacent images have a strong correlation in serial images. Our 4S repeats training, inference, and pseudo-labeling using a minimal number of teacher labels and performs segmentation on all slices. ResultOur experiments using two types of serial section images showed effectiveness in terms of both quality and quantity. In addition, we experimentally clarified the effect of the number and position of teacher labels on performance. Comparison with existing methodsCompared with supervised learning when a small number of labeled data was obtained, the performance of the proposed method was shown to be superior. ConclusionOur 4S leverages a limited number of labeled data and a large amount of unlabeled data to extract neural regions from serial image stacks in a transductive setting. We plan to develop this method as a core module of a general-purpose annotation tool in our future work.

Soft X-ray Ptychography Chemical Imaging of Degradation in a Composite Surface-Reconstructed Li-Rich Cathode.

The capability in spatially resolving the interactions between components in lithium (Li)-ion battery cathodes, especially correlating chemistry and electronic structure, is challenging but critical for a better understanding of complex degradation mechanisms for rational developments. X-ray spectro-ptychography and conventional synchrotron-based scanning transmission X-ray microscopy image stacks are the most powerful probes for studying the distribution and chemical state of cations in degraded Li-rich cathodes. Herein, we propose a chemical approach with a spatial resolution of around 5.6 nm to imaging degradation heterogeneities and interplay among components in degraded Li-rich cathodes. Through the chemical imaging reconstruction of the degraded Li-rich cathodes, fluorine (F) ions incorporated into the lattice during charging/discharging processes are proved and strongly correlate with the manganese (Mn) dissolution and oxygen loss within the secondary particles and impact the electronic structure. Otherwise, the electrode-electrolyte interphase component, scattered LiF particles (100-500 nm) along with the MnF2 layer, is also visualized between the primary particles inside the secondary particles of the degraded cathodes. The results provide direct visual evidence for the Li-rich cathode degradation mechanisms and demonstrate that the low-energy ptychography technique offers a superior approach for high-resolution battery material characterization.

Three-dimensional computation of fibre orientation, diameter and branching in segmented image stacks of fibrous networks.

Fibre topography of the extracellular matrix governs local mechanical properties and cellular behaviour including migration and gene expression. While quantifying properties of the fibrous network provides valuable data that could be used across a breadth of biomedical disciplines, most available techniques are limited to two dimensions and, therefore, do not fully capture the architecture of three-dimensional (3D) tissue. The currently available 3D techniques have limited accuracy and applicability and many are restricted to a specific imaging modality. To address this need, we developed a novel fibre analysis algorithm capable of determining fibre orientation, fibre diameter and fibre branching on a voxel-wise basis in image stacks with distinct fibre populations. The accuracy of the technique is demonstrated on computer-generated phantom image stacks spanning a range of features and complexities, as well as on two-photon microscopy image stacks of elastic fibres in bovine tendon and dermis. Additionally, we propose a measure of axial spherical variance which can be used to define the degree of fibre alignment in a distribution of 3D orientations. This method provides a useful tool to quantify orientation distributions and variance on image stacks with distinguishable fibres or fibre-like structures.

AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks

Alignment of stacks of serial images generated by Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) is generally performed using translations only, either through slice-by-slice alignments with SIFT or alignment by template matching. However, limitations of these methods are two-fold: the introduction of a bias along the dataset in the z-direction which seriously alters the morphology of observed organelles and a missing compensation for pixel size variations inherent to the image acquisition itself. These pixel size variations result in local misalignments and jumps of a few nanometers in the image data that can compromise downstream image analysis. We introduce a novel approach which enables affine transformations to overcome local misalignments while avoiding the danger of introducing a scaling, rotation or shearing trend along the dataset. Our method first computes a template dataset with an alignment method restricted to translations only. This pre-aligned dataset is then smoothed selectively along the z-axis with a median filter, creating a template to which the raw data is aligned using affine transformations. Our method was applied to FIB-SEM datasets and showed clear improvement of the alignment along the z-axis resulting in a significantly more accurate automatic boundary segmentation using a convolutional neural network.

Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation

Microscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neuronal morphologies as well as the often low quality and ambiguity of the images. Here we present a novel method we developed in an effort to improve the robustness of digital reconstruction against these complicating factors. The method is based on probabilistic filtering by sequential Monte Carlo estimation and uses prediction and update models designed specifically for tracing neuronal branches in microscopic image stacks. Moreover, it uses multiple probabilistic traces to arrive at a more robust, ensemble reconstruction. The proposed method was evaluated on fluorescence microscopy image stacks of single neurons and dense neuronal networks with expert manual annotations serving as the gold standard, as well as on synthetic images with known ground truth. The results indicate that our method performs well under varying experimental conditions and compares favorably to state-of-the-art alternative methods.

VIRMISCO – The Virtual Microscope Slide Collection

Digitisation allows scientists rapid access to research objects. For transparent to semi-transparent three-dimensional microscopic objects, such as microinvertebrates or small body parts of organisms, available databases are scarce. Most mounting media used for permanent microscope slides deteriorate after some years or decades, eventually leading to total damage and loss of the object. However, restoration is labour-intensive, and often the composition of the mounting media is not known. A digital preservation of important material, especially types, is important and an urgent need. The Virtual Microscope Slide Collection – VIRMISCO project has developed recommendations for taking microscopic image stacks of three-dimensional objects, depositing and presenting such series of digital image files or z-stacks as an online platform. The core of VIRMISCO is an online viewer, which enables the user to virtually focus through an object online as if using a real microscope. Additionally, VIRMISCO offers features such as search, rotating, zooming, measuring, changing brightness or contrast, taking snapshots, leaving feedback as well as downloading complete z-stacks as jpeg files or video file. The open source system can be installed by any institution and can be linked to common database or images can be sent to the Senckenberg Museum of Natural History Görlitz. The benefits of VIRMISCO are the preservation of important or fragile material, to avoid loan, to act as a digital archive for image files and to allow determination by experts from the distance, as well as providing reference libraries for taxonomic research or education and providing image series as online supplementary material for publications or digital vouchers of specimens of molecular investigations are relevant applications for VIRMISCO.

Image processing for precise three-dimensional registration and stitching of thick high-resolution laser-scanning microscopy image stacks

The possible depth of imaging of laser-scanning microscopy is limited not only by the working distances of objective lenses but also by image degradation caused by attenuation and diffraction of light passing through the specimen. To tackle this problem, one can either flip the sample to record images from both sides of the specimen or consecutively cut off shallow parts of the sample after taking serial images of certain thickness. Multiple image substacks acquired in these ways should be combined afterwards to generate a single stack. However, subtle movements of samples during image acquisition cause mismatch not only in the translation along x-, y-, and z-axes and rotation around z-axis but also tilting around x- and y-axes, making it difficult to register the substacks precisely. In this work, we developed a novel approach called 2D-SIFT-in-3D-Space using Scale Invariant Feature Transform (SIFT) to achieve robust three-dimensional matching of image substacks. Our method registers the substacks by separately fixing translation and rotation along x-, y-, and z-axes, through extraction and matching of stable features across two-dimensional sections of the 3D stacks. To validate the quality of registration, we developed a simulator of laser-scanning microscopy images to generate a virtual stack in which noise levels and rotation angles are controlled with known parameters. We illustrate quantitatively the performance of our approach by registering an entire brain of Drosophila melanogaster consisting of 800 sections. Our approach is also demonstrated to be extendable to other types of data that share large dimensions and need of fine registration of multiple image substacks. This method is implemented in Java and distributed as ImageJ/Fiji plugin. The source code is available via Github (http://www.creatis.insa-lyon.fr/site7/fr/MicroTools).

The 3D Retinal Capillary Circulation in Pigs Reveals a Predominant Serial Organization.

To establish a model of the retinal capillary circulation in pigs, which in many aspects is close to the human retina. Using high density confocal microscopy image stacks of immunolabeled porcine retinal whole mounts, microvessels close to the optic nerve head were traced in three dimensions. The direction of flow of individual capillaries was deduced from their arteriolar and/or venous connections. From major arteries, second-order arteries traversed the nerve fiber layer and resolved exclusively into the superficial vascular plexus (SVP), which dichotomized the blood flow between radial peripapillary capillaries (RPCs) on one side and the intermediate (IVP) and deep vascular plexus (DVP) on the other. Each RPC was supplied by one or several capillaries from the SVP and drained to the IVP or DVP. The DVP was a mosaic of approximately 300 to 600 μm wide anastomotic watersheds, each drained by one or two venules connected to major veins. A presumptive direction of flow could be determined for >90% of capillaries. These results suggest a model of the capillary circulation in which the three microvessel layers are serially organized with RPCs are in parallel between the SVP and IVP or DVP. In the peripapillary retina of pigs, microvascular layers have a serial arrangement, with RPCs emerging from the SVP and draining to the IVP or DVP; hence, connected in parallel of this scheme. The bulk of flow, therefore, traverses the SVP and DVP successively. This organization contributes to the higher oxygen saturation in the SVP and RPCs than in the DVP. Physiopathologic implications of this model regarding retinal diseases are discussed.

Cell Population Tracking in a Honeycomb Structure Using an IMM Filter Based 3D Local Graph Matching Model.

Developing algorithms for plant cell population tracking is very critical for the modeling of plant cell growth pattern and gene expression dynamics. The tracking of plant cells in microscopic image stacks is very challenging for several reasons: (1) plant cells are densely packed in a specific honeycomb structure; (2) they are frequently dividing; and (3) they are imaged in different layers within 3D image stacks. Based on an existing 2D local graph matching algorithm, this paper focuses on building a 3D plant cell matching model, by exploiting the cells' 3D spatiotemporal context. Furthermore, the Interacting Multi-Model filter (IMM) is combined with the 3D local graph matching model to track the plant cell population simultaneously. Because our tracking algorithm does not require the identification of "tracking seeds", the tracking stability and efficiency are greatly enhanced. Last, the plant cell lineages are achieved by associating the cell tracklets, using a maximum-a-posteriori (MAP) method. Compared with the 2D matching method, the experimental results on multiple datasets show that our proposed approach does not only greatly improve the tracking accuracy by 18 percent, but also successfully tracks the plant cells located at the high curvature primordial region, which is not addressed in previous work.

VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations

Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections. VIPAR (volume information-based histopathological analysis by 3D reconstruction and data extraction), a light-sheet microscopy-based approach for the analysis of tissue biopsies, is based on digital reconstruction and visualization of microscopic image stacks. VIPAR allows semiautomated segmentation of the vasculature and subsequent nonbiased extraction of characteristic vessel shape and connectivity parameters. We applied VIPAR to analyze biopsies from healthy lymphedematous and lymphangiomatous skin. Digital 3D reconstruction provided a directly visually interpretable, comprehensive representation of the lymphatic and blood vessels in the analyzed tissue volumes. The most conspicuous features were disrupted lymphatic vessels in lymphedematous skin and a hyperplasia (4.36-fold lymphatic vessel volume increase) in the lymphangiomatous skin. Both abnormalities were detected by the connectivity analysis based on extracted vessel shape and structure data. The quantitative evaluation of extracted data revealed a significant reduction of lymphatic segment length (51.3% and 54.2%) and straightness (89.2% and 83.7%) for lymphedematous and lymphangiomatous skin, respectively. Blood vessel length was significantly increased in the lymphangiomatous sample (239.3%). VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin. Its application is not limited to the vascular systems or skin. Max Planck Society, DFG (SFB 656), and Cells-in-Motion Cluster of Excellence EXC 1003.

SynEM, automated synapse detection for connectomics.

Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.

Three-dimensional modeling of human placental terminal villi

IntroductionPlacental transport is the main factor affecting the health and development of the fetus. Due to the placenta's geometrical and mathematical complexity, the structure-function relations of placental terminal villi have not been successfully modeled. Hence, a novel modeling approach is proposed. MethodsComputational models of four different specimens were generated from the three-dimensional reconstruction of confocal laser scanning microscopic image stacks. To evaluate the capabilities of the proposed methodology, stationary oxygen diffusion transport was calculated in the terminal villus volumes. ResultsThe reconstructions automatically provided the spatial arrangement of the fetal capillaries inside the terminal villi. The surface and volume ratios between the fetal capillaries and the villus were also calculated, and the effects of model parameters on the placental diffusive capacity were assessed by parametric analysis. DiscussionThe potential of three-dimensional reconstructions combined with finite element analysis as a research tool for the human placenta was tested. The methodology herein could serve in the future as a simulation platform for complicated in vivo and in vitro scenarios.