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The analysis of neural connection maps on the whole brain scale is an important basis for revealing the working principle of the brain, which is conducive to promoting the development of a new generation of intelligent industry. Microscale reconstruction of the brain using electron microscopy is an effective method to restore neural three-dimensional connections at the nanoscale. At present, with the continuous development of brain science and microscopic imaging technology, the volume of brain microscale reconstruction becomes bigger and the number of brain tissue serial sections is much more. Rapid and accurate detection of a large number of collected brain tissue serial sections is an important basis of automatic electron microscopic imaging process. Manually detecting these serial sections takes a lot of labor and time. This paper proposes an automatic detection algorithm based on deep learning, which can accurately detect serial sections, provide accurate section positions for automatic electron microscope imaging, speed up the automatic imaging process, reduce manual participation, save labor and time.

As a part of the automatic electron microscopic imaging process in brain microscale reconstruction, the main achievements of this paper include: 1)To solve the problems that abnormal sections such as wrinkles and damage are difficult to distinguish from normal sections due to their similar shapes, and the boundaries between sections are not clear and difficult to distinguish due to the adhesion between sections, a multi-frequency channel attention mechanism is proposed. The frequency domain components of different channels are used as channel attention to enhance the important information in the feature map and enhance the networks ability to recognize sections. The experimental results show that the method can achieve more accurate detection of sequence sections. 2)An active learning algorithm is proposed for the situation that the neural network needs to be fine-tuned using new dataset when facing the new brain tissue serial sections detection task. A loss prediction module is added to the instance segmentation network to predict the loss. The loss predicted is added to the category posteriori probability weighted instance segmentation mask uncertainty as the value of a sample. Selecting the samples with large value to label constitutes the dataset, which enables network to have the expected performanc. Experimental results demonstrate that this method can achieve the desired performance of the network with only 30% samples as dataset. 3)For brain tissue sections automatic detection algorithm in the application of automatic data collection process, a software system is proposed. The system uses detection algorithm to detect serial sections and result can be used directly for subsequent acquisition of a particular format. Thus the detection algorithm is embedded in the automatic electron microscopic imaging process in brain microscale reconstruction.

Keyword脑微观重建 序列切片 实例分割 主动学习
Document Type学位论文
Recommended Citation
GB/T 7714
孙国栋. 基于深度学习的脑组织序列切片自动检测[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.
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孙国栋-基于深度学习的脑组织序列切片自动(21637KB)学位论文 限制开放CC BY-NC-SA
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