A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 2
Feb.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Long Cheng, Weizhou Liu, Chao Zhou, Yongxiang Zou and Zeng-Guang Hou, "Automated Silicon-Substrate Ultra-Microtome for Automating the Collection of Brain Sections in Array Tomography," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 389-401, Feb. 2021. doi: 10.1109/JAS.2021.1003829
 Citation: Long Cheng, Weizhou Liu, Chao Zhou, Yongxiang Zou and Zeng-Guang Hou, "Automated Silicon-Substrate Ultra-Microtome for Automating the Collection of Brain Sections in Array Tomography," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 389-401, Feb. 2021.

Automated Silicon-Substrate Ultra-Microtome for Automating the Collection of Brain Sections in Array Tomography

doi: 10.1109/JAS.2021.1003829
Funds:  This work was supported in part by the National Natural Science Foundation of China (61873268, 62025307, U1913209) and the Beijing Natural Science Foundation (JQ19020)
• Understanding the structure and working principle of brain neural networks requires three-dimensional reconstruction of brain tissue samples using array tomography method. In order to improve the reconstruction performance, the sequence of brain sections should be collected with silicon wafers for subsequent electron microscopic imaging. However, the current collection of brain sections based on silicon substrate involve mainly manual collection, which requires the involvement of automation techniques to increase collection efficiency. This paper presents the design of an automatic collection device for brain sections. First, a novel mechanism based on circular silicon substrates is proposed for collection of brain sections; second, an automatic collection system based on microscopic object detection and feedback control strategy is proposed. Experimental results verify the function of the proposed collection device. Three objects (brain section, left baffle, right baffle) can be detected from microscopic images by the proposed detection method. Collection efficiency can be further improved with position feedback of brain sections well. It has been experimentally verified that the proposed device can well fulfill the task of automatic collection of brain sections. With the help of the proposed automatic collection device, human operators can be partially liberated from the tedious manual collection process and collection efficiency can be improved.

• 1 VGG16 is one popular and effective deep learning method for the image classification. VGG16 includes 16 hidden layers (13 convolution layers and 3 fully connected layers). The size of the convolution kernel ($\substack {3\times 3}$) and the size of the pooling ($\substack {2\times 2 }$) are set to be the same for the entire VGG16 network.

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通讯作者: 陈斌, bchen63@163.com
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沈阳化工大学材料科学与工程学院 沈阳 110142

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Highlights

• A novel mechanism based on circular silicon substrates is proposed for collection of brain section
• An automatic collection system based on microscopic object detection and feedback control strategy is proposed
• With the proposed automatic collection device, human operators can be partially liberated from the tedious manual collection process

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