Recently, due to the rapid development of electron microscope (EM) with its high resolution, stacks delivered by EM can be used to analyze a variety of components that are critical to understand brain function. Since synaptic study is essential in neurobiology and can be analyzed by EM stacks, the automated routines for reconstruction of synapses based on EM Images can become a very useful tool for analyzing large volumes of brain tissue and providing the ability to understand the mechanism of brain. In this article, we propose a novel automated method to realize 3D reconstruction of synapses for Automated Tapecollecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) with deep learning. Being different from other reconstruction algorithms, which employ classifier to segment synaptic clefts directly. We utilize deep learning method and segmentation algorithm to obtain synaptic clefts as well as promote the accuracy of reconstruction. The proposed method contains five parts: (1) using modified Moving Least Square (MLS) deformation algorithm and Scale Invariant Feature Transform (SIFT) features to register adjacent sections, (2) adopting Faster Region Convolutional Neural Networks (Faster R-CNN) algorithm to detect synapses, (3) utilizing screening method which takes context cues of synapses into consideration to reduce the false positive rate, (4) combining a practical morphology algorithm with a suitable fitting function to segment synaptic clefts and optimize the shape of them, (5) applying the plugin in FIJI to show the final 3D visualization of synapses. Experimental results on ATUM-SEM images demonstrate the effectiveness of our proposed method.
Microscopic image registration is a key component of the neural structure reconstruction with serial sections of neural
tissue. The goal of microscopic neural image registration is to recover the 3D continuity and geometrical properties of
specimen. During image registration, various distortions need to be corrected, including image rotation, translation,
tissue deformation et.al, which come from the procedure of sample cutting, staining and imaging. Furthermore, there is
only certain similarity between adjacent sections, and the degree of similarity depends on local structure of the tissue and
the thickness of the sections. These factors make the microscopic neural image registration a challenging problem.
To tackle the difficulty of corresponding landmarks extraction, we introduce a novel image registration method for
Scanning Electron Microscopy (SEM) images of serial neural tissue sections based on the structure of mitochondria. The
ellipsoidal shape of mitochondria ensures that the same mitochondria has similar shape between adjacent sections, and its
characteristic of broad distribution in the neural tissue guarantees that landmarks based on the mitochondria distributed
widely in the image. The proposed image registration method contains three parts: landmarks extraction between
adjacent sections, corresponding landmarks matching and image deformation based on the correspondences. We
demonstrate the performance of our method with SEM images of drosophila brain.
Extracting the structure of single neurons is critical for understanding how they function within the neural circuits.
Recent developments in microscopy techniques, and the widely recognized need for openness and standardization
provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons. In
order to look into the fine structure of neurons, we use the Automated Tape-collecting Ultra Microtome Scanning
Electron Microscopy (ATUM-SEM) to get images sequence of serial sections of animal brain tissue that densely
packed with neurons. Different from other neuron reconstruction method, we propose a method that enhances the SEM
images by detecting the neuronal membranes with deep convolutional neural network (DCNN) and segments single
neurons by active contour with group shape similarity. We joint the segmentation and tracing together and they interact
with each other by alternate iteration that tracing aids the selection of candidate region patch for active contour
segmentation while the segmentation provides the neuron geometrical features which improve the robustness of tracing.
The tracing model mainly relies on the neuron geometrical features and is updated after neuron being segmented on
the every next section. Our method enables the reconstruction of neurons of the drosophila mushroom body which is
cut to serial sections and imaged under SEM. Our method provides an elementary step for the whole reconstruction of