9 March 2017 3D reconstruction of synapses with deep learning based on EM Images
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Abstract
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.
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Chi Xiao, Chi Xiao, Qiang Rao, Qiang Rao, Dandan Zhang, Dandan Zhang, Xi Chen, Xi Chen, Hua Han, Hua Han, Qiwei Xie, Qiwei Xie, } "3D reconstruction of synapses with deep learning based on EM Images", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101324N (9 March 2017); doi: 10.1117/12.2254282; https://doi.org/10.1117/12.2254282
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