A Gaussian Filter-Based 3D Registration Method for Series Section Electron Microscopy
Zhang, Zhenbang Li, Hongjia Xu, Zhiqiang Meng, Wenjia Han, Renmin
Zhang, Zhenbang
Li, Hongjia
Xu, Zhiqiang
Meng, Wenjia
Han, Renmin
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Machine Learning
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Conference proceeding
Date
2025
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English
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Abstract
Series Section Electron Microscopy (ssEM) is a crucial technique for visualizing three-dimensional (3D) biological structures, which involves collecting electron microscopy images from a series of biological sections along the z-axis and reconstructing the 3D structure. 3D registration is an essential step in ssEM, designed to eliminate axial misalignment and nonlinear distortions introduced during sample sectioning. A significant challenge in 3D registration is eliminating nonlinear distortions while preserving natural deformations. In this paper, we present a new formulation of the 3D registration problem from a frequency domain perspective and propose a Gaussian filtering-based 3D registration method, which defines 3D registration as a superposition problem of high-frequency and low-frequency components. We extend the concept of a one-dimensional Gaussian filter to three-dimensional image stacks and integrate it with optical flow networks to consolidate the deformation field within the receptive field. Extensive experiments demonstrate that our method can successfully decouple nonlinear distortions and natural deformations in the frequency domain, proving superior to existing methods in rapidly and accurately eliminating nonlinear distortions and restoring biological structures, and has the potential to be extended to large datasets.
Citation
Z. Zhang, H. Li, Z. Xu, W. Meng, and R. Han, “A Gaussian Filter-Based 3D Registration Method for Series Section Electron Microscopy,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 1, pp. 1156–1164, Apr. 2025, doi: 10.1609/AAAI.V39I1.32103.
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Proceedings of the AAAI Conference on Artificial Intelligence
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Association for the Advancement of Artificial Intelligence