An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy
ABSTRACT The analysis of microcircuitry (the connectivity at the level of individual neuronal processes and synapses), which is indispensable for our understanding of brain function, is based on serial transmission electron microscopy (TEM) or one of its modern variants. Due to technical limitations, most previous studies that used serial TEM recorded relatively small stacks of individual neurons. As a result, our knowledge of microcircuitry in any nervous system is very limited. We applied the software package TrakEM2 to reconstruct neuronal microcircuitry from TEM sections of a small brain, the early larval brain of Drosophila melanogaster. TrakEM2 enables us to embed the analysis of the TEM image volumes at the microcircuit level into a light microscopically derived neuro-anatomical framework, by registering confocal stacks containing sparsely labeled neural structures with the TEM image volume. We imaged two sets of serial TEM sections of the Drosophila first instar larval brain neuropile and one ventral nerve cord segment, and here report our first results pertaining to Drosophila brain microcircuitry. Terminal neurites fall into a small number of generic classes termed globular, varicose, axiform, and dendritiform. Globular and varicose neurites have large diameter segments that carry almost exclusively presynaptic sites. Dendritiform neurites are thin, highly branched processes that are almost exclusively postsynaptic. Due to the high branching density of dendritiform fibers and the fact that synapses are polyadic, neurites are highly interconnected even within small neuropile volumes. We describe the network motifs most frequently encountered in the Drosophila neuropile. Our study introduces an approach towards a comprehensive anatomical reconstruction of neuronal microcircuitry and delivers microcircuitry comparisons between vertebrate and insect neuropile.
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Article: An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy
- SourceAvailable from: Dmitry Laptev
- "It recovers the crisp image of these structures and facilitates recognition of neural structures. The experiments on Drosophila first instar larva ventral nerve cord (VNC) dataset Cardona and et al. (2010) demonstrate significant improvement over the baselines. "
Conference Paper: SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data[Show abstract] [Hide abstract]
ABSTRACT: In biological imaging the data is often represented by a sequence of anisotropic frames — the resolution in one dimension is significantly lower than in the other dimensions. E.g. in electron microscopy it arises from the thickness of a scanned section. This leads to blurred images and raises problems in tasks like neuronal image segmentation. We present the details and additional evaluation of an approach originally introduced in Laptev et al. (2014) called SuperSlicing to decompose the observed frame into a sequence of plausible hidden sub-frames. Based on sub-frame decomposition by SuperSlicing we propose a novel automated method to perform neuronal structure segmentation. We test our approach on a popular connectomics benchmark, where SuperSlicing preserves topological structures significantly better than other algorithms. We also generalize the approach for video anisotropicity that comes from the long exposure time and show that our method outperforms baseline methods on a reconstruction of low frame rate videos of natural scenes.Neural Connectomics Workshop, ECML-PKDD 2014; 09/2014
- "Serial section transmission electron microscopy (ssTEM)  of brain tissue is an important example. "
Conference Paper: Superslicing frame restoration for anisotropic sstem[Show abstract] [Hide abstract]
ABSTRACT: In biological imaging the data is often represented by a sequence of anisotropic frames — the resolution in one dimension is significantly lower than in the other dimensions. E.g. in electron microscopy it arises from the thickness of a scanned section. This leads to blurred images and raises problems in tasks like neuronal image segmentation. We present an approach called SuperSlicing to decompose the observed frame into a sequence of plausible hidden sub-frames. Based on sub-frame decomposition by SuperSlicing we propose a novel automated method to perform neuronal structure segmentation. We test our approach on a popular benchmark, where SuperSlicing preserves topological structures significantly better than other algorithms.2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014); 04/2014
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- "With the recent advancements in the automated collection of large image datasets of nano-scale Electron Microscopy (EM) images of brain tissue  , have resulted in interest in the neuroscience community to develop computational algorithms for an automated analysis system in order to understand the structures and connectivity more accurately and automatically . The development of such a system is important as it would help neuroscientists better understand the maps of the partial or the complete brain . To develop such a system, the first step is to find connectomes in the tissue in order to understand the complete connectivity in the brain map. "
ABSTRACT: In this paper, we present a Support Vector Machine (SVM) based pixel classifier for a semi-automated segmentation algorithm to detect neuronal membrane structures in stacks of electron microscopy images of brain tissue samples. This algorithm uses high-dimensional feature spaces extracted from center-surrounded patches, and some distinct edge sensitive features for each pixel in the image, and a training dataset for the segmentation of neuronal membrane structures and background. Some threshold conditions are later applied to remove small regions, which are below a certain threshold criteria, and morphological operations, such as the filling of the detected objects, are done to get compactness in the objects. The performance of the segmentation method is calculated on the unseen data by using three distinct error measures: pixel error, wrapping error, and rand error, and also a pixel by pixel accuracy measure with their respective ground-truth. The trained SVM classifier achieves the best precision level in these three distinct errors at 0.23, 0.016 and 0.15, respectively; while the best accuracy using pixel by pixel measure reaches 77% on the given dataset. The results presented here are one step further towards exploring possible ways to solve these hard problems, such as segmentation in medical image analysis. In the future, we plan to extend it as a 3D segmentation approach for 3D datasets to not only retain the topological structures in the dataset but also for the ease of further analysis.Sixth International Conference on Machine Vision (ICMV 2013), London, United Kingdom; 11/2013