3D Registration-Guided Deformable Residual Inpainting for ssEM Restoration

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ID: 314698
2026
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Abstract
Abstract Motivation Serial section Electron Microscopy (ssEM) is essential for studying biological cell structures at nanometer resolution. However, Supporting Film Folding (SFF) degradation frequently occurs during sample preparation, causing structural distortions and information loss that severely impair downstream analyses such as 3D reconstruction and neuron segmentation. Results We propose RegInpaint, a novel recovery framework that jointly addresses deformation correction and missing-information restoration caused by SFF degradation. RegInpaint formulates SFF recovery as a joint problem of 3D elastic registration and image inpainting, providing a generalizable solution for ssEM restoration. Experiments on four EM datasets show that RegInpaint consistently outperforms existing methods in image restoration quality and significantly improves neuron segmentation accuracy. Availability Source code is freely available at https://github.com/zhangzhenbang2021/RegInpaint.git.
Reference Key
openalex_W7162095686 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhenbang Zhang, Jingtong Feng, Hongjia Li, Haythem El-Messiry, Zhiqiang Xu, Renmin Han
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag329
URL
https://doi.org/10.1093/bioinformatics/btag329
Keywords Keywords not found

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