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Published on: September 25, 2019
3D registration-guided deformable residual inpainting for ssEM restoration
Zhenbang Zhang1,2,3, Jingtong Feng2,4, Hongjia Li5
1Shenzhen Research Institute of Shandong University, Shenzhen, 518000, China.
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 And Implementation:
Source code is freely available at https://github.com/zhangzhenbang2021/RegInpaint.git.
