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Neuronal Morphological Model-Driven Image Registration for Serial Electron Microscopy Sections | |
Zhou FX(周芳旭) | |
发表期刊 | Frontiers in Human Neuroscience |
2022 | |
页码 | 20 |
摘要 | Registration of a series of the two-dimensional electron microscope (EM) images of the brain tissue into volumetric form is an important technique that can be used for neuronal circuit reconstruction. However, complex appearance changes of neuronal morphology in adjacent sections bring difficulty in finding correct correspondences, making serial section neural image registration challenging. To solve this problem, we consider whether there are such stable "markers" in the neural images to alleviate registration difficulty. In this paper, we employ the spherical deformation model to simulate the local neuron structure and analyze the relationship between registration accuracy and neuronal structure shapes in two adjacent sections. The relevant analysis proves that regular circular structures in the section images are instrumental in seeking robust corresponding relationships. Then, we design a new serial section image registration framework driven by this neuronal morphological model, fully utilizing the characteristics of the anatomical structure of nerve tissue and obtaining more reasonable corresponding relationships. Specifically, we leverage a deep membrane segmentation network and neural morphological physical selection model to select the stable rounded regions in neural images. Then, we combine feature extraction and global optimization of correspondence position to obtain the deformation field of multiple images. Experiments on real and synthetic serial EM section neural image datasets have demonstrated that our proposed method could achieve more reasonable and reliable registration results, outperforming the state-of-the-art approaches in qualitative and quantitative analysis. |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48797 |
专题 | 脑图谱与类脑智能实验室_微观重建与智能分析 |
推荐引用方式 GB/T 7714 | Zhou FX. Neuronal Morphological Model-Driven Image Registration for Serial Electron Microscopy Sections[J]. Frontiers in Human Neuroscience,2022:20. |
APA | Zhou FX.(2022).Neuronal Morphological Model-Driven Image Registration for Serial Electron Microscopy Sections.Frontiers in Human Neuroscience,20. |
MLA | Zhou FX."Neuronal Morphological Model-Driven Image Registration for Serial Electron Microscopy Sections".Frontiers in Human Neuroscience (2022):20. |
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2022-SCI-周芳旭-Neurona(5155KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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