Correction of image distortion in large-field ssEM stitching by an unsupervised intermediate-space solving network
文献类型：期刊论文
| 作者 | He, Bintao3,4; Zhang, Yan2; Zhang, Fa1; Han, Renmin4 |
| 刊名 | BIOINFORMATICS
 |
| 出版日期 | 2022-10-14 |
| 卷号 | 38期号:20页码:4797-4805 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btac566 |
| 英文摘要 | Motivation: Serial-section electron microscopy (ssEM) is a powerful technique for cellular visualization, especially for large-scale specimens. Limited by the field of view, a megapixel image of whole-specimen is regularly captured by stitching several overlapping images. However, suffering from distortion by manual operations, lens distortion or electron impact, simple rigid transformations are not adequate for perfect mosaic generation. Non-linear deformation usually causes 'ghosting' phenomenon, especially with high magnification. To date, existing microscope image processing tools provide mature rigid stitching methods but have no idea with local distortion correction. Results: In this article, following the development of unsupervised deep learning, we present a multi-scale network to predict the dense deformation fields of image pairs in ssEM and blend these images into a clear and seamless montage. The model is composed of two pyramidal backbones, sharing parameters and interacting with a set of registration modules, in which the pyramidal architecture could effectively capture large deformation according to multi-scale decomposition. A novel 'intermediate-space solving' paradigm is adopted in our model to treat inputted images equally and ensure nearly perfect stitching of the overlapping regions. Combining with the existing rigid transformation method, our model further improves the accuracy of sequential image stitching. Extensive experimental results well demonstrate the superiority of our method over the other traditional methods. |
| 资助项目 | National Key Research and Development Program of China[2021YFF0704300] ; National Key Research and Development Program of China[2020YFA0712401] ; National Natural Science Foundation of China[62072280] ; National Natural Science Foundation of China[61932018] ; National Natural Science Foundation of China[62072441] ; National Natural Science Foundation of China[31730023] ; National Natural Science Foundation of China[31521002] ; National Natural Science Foundation of China[31925026] ; Chinese Academy of Sciences (CAS)[XDB37010100] ; National Laboratory of Biomacromolecules of China[2019KF07] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000857451900001 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19820]  |
| 专题 | 中国科学院计算技术研究所期刊论文 |
| 通讯作者 | Zhang, Fa; Han, Renmin |
| 作者单位 | 1.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Ctr Biol Imaging, Inst Biophys, Beijing 100190, Peoples R China 3.BioMap Inc, Beijing 100086, Peoples R China 4.Shandong Univ, Res Ctr Math & Interdisciplinary Sci, Frontiers Sci Ctr Nonlinear Expectat, Minist Educ, Jinan 266000, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Bintao,Zhang, Yan,Zhang, Fa,et al. Correction of image distortion in large-field ssEM stitching by an unsupervised intermediate-space solving network[J]. BIOINFORMATICS,2022,38(20):4797-4805. |
| APA | He, Bintao,Zhang, Yan,Zhang, Fa,&Han, Renmin.(2022).Correction of image distortion in large-field ssEM stitching by an unsupervised intermediate-space solving network.BIOINFORMATICS,38(20),4797-4805. |
| MLA | He, Bintao,et al."Correction of image distortion in large-field ssEM stitching by an unsupervised intermediate-space solving network".BIOINFORMATICS 38.20(2022):4797-4805. |
入库方式： OAI收割
来源：计算技术研究所
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