|Thesis Advisor||韩华 研究员 ; 谢启伟 教授|
|Place of Conferral||北京|
|Keyword||微观脑连接图谱 电子显微图像 卷积神经网络 深度学习 图像分割|
Microscopic connectome reconstruction is an important step in exploring and understanding the neural function of brain. The reconstruction work consists of imaging the brain tissue with electron microscope, identifying and reconstructing the neurons and ultrastructure, and obtaining the microstructure of the brain neural circuit, which is of great significance for the research of neural circuit, neurogenic diseases, neural function and artificial intelligence. However, the reconstruction of microscopic connectome is a tough task due to several challenges, e.g., the complex background of EM (electron microscopy) image, the various sizes and shapes of the ultrastructure, etc. It is difficult to obtain satisfactory results by applying the existing deep learning-based methods. Therefore, the current reconstruction work relies heavily on manual label work, it takes thousands of human work hours to achieve large-scale microscopic connectome reconstruction.
In this dissertation, we mainly research the reconstruction algorithms of synapses, mitochondria and neurons in the microscopic connectome. In view of the shortcomings of the existing methods, we propose deep learning networks appropriate for the morphology characteristics of the aforementioned targets, which break through the technical bottlenecks in automatic identification of ultrastructure and dense segmentation of neurons, it also reduces the dependence on labor while improving the accuracy and efficiency. The main contributions are listed as follows:
1. To tackle the low accuracy and low efficiency of synapses reconstruction algorithm under the complex background, an effective coarse-to-fine automated pipeline is proposed for synapses reconstruction. The proposed method first adopts the detection network based on deep learning to detect synapses and uses the z-continuity of synapses to reduce false positives. Subsequently, it combines the Dijkstra algorithm with the GrabCut algorithm to obtain the segmentation of synaptic clefts. The experimental results in ATUM-SEM and FIB-SEM datasets demonstrate the effectiveness and efficiency of the proposed method, and the average precision of our detection (92.8% in anisotropy, 93.5% in isotropy) and segmentation (88.6% in anisotropy, 93.0% in isotropy) suggests that our method achieves state-of-the-art results. In addition, we utilize this method to obtain the 3D reconstruction and morphological statistics of synapses in large volume EM data, which provide reliable analytical data for neurologists to research synapses.
2. To tackle the variety of target structures and noise in mitochondria reconstruction, the method based on 3D supervised full convolution network is proposed to reconstruction mitochondria. In this method, the 3D convolution network could effectively utilize the three-dimensional information of the EM data. The structure combines U-Net and residual convolution module is used to fully integrate the features of different scales so as to identify mitochondria of different shapes and sizes. The strategy of deep supervision is helpful to avoid gradient vanishing in training process. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of the proposed method. On this basis, we obtain the 3D reconstruction and morphological statistics of mitochondria in large EM data, and we reconstruct the mitochondria in the cell body and dendrites of mouse motor cortex. The reconstruction results prove that mitochondria are connected by ``mitochondrial conduits" in dendrites and provide neurologists a new way to study the function of mitochondria in neurons.
3. For the problems of complex background and distinct difference of neuronal structure in neuron reconstruction, we propose an effective neuron reconstruction method based on context residual network. It utilizes multi-scale contextual cues to distinguish neuron membranes from other organelles. The sub-pixel convolution layer in the network is used to reduce the loss of neuron membrane information in up-sampling process. Furthermore, Lifted Multicut is utilized to obtain the 3D segmentation results of neurons. The proposed method now ranks 2/171 in the ISBI EM segmentation challenge and achieves the best result in the mouse piriform cortex dataset. We then apply this method to dense reconstruct a volume of 33*30*40 um3 EM data from mouse cortex and obtain the most of the neuronal structure with a small amount of manual verification.
|Funding Project||Strategic Priority Research Program of the CAS[XDB02060001] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; Special Program of Beijing Municipal Science and Technology Commission[Z181100000118002] ; Special Program of Beijing Municipal Science & Technology Commission[Z161100000216146] ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Special Program of Beijing Municipal Science & Technology Commission[Z161100000216146] ; Special Program of Beijing Municipal Science and Technology Commission[Z181100000118002] ; Scientific Instrument Developing Project of Chinese Academy of Sciences[YZ201671] ; Strategic Priority Research Program of the CAS[XDB02060001]|
|肖驰. 基于深度学习的微观脑连接图谱重建方法研究[D]. 北京. 中国科学院大学,2019.|
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