Correcting Non-Uniform Milling in FIB-SEM Images with Unsupervised Cross-Plane Image-to-Image Translation
Li, Y.; Kreinin, Y.; Huang, S.; Schomburg, E. W.; Chklovskii, D. B.; Pfister, H.; Wu, J.
Show abstract
MotivationFocused Ion Beam Scanning Electron Microscopy (FIB-SEM) is an advanced Volume Electron Microscopy technology with growing applications, featuring thinner sectioning compared to other Volume Electron Microscopes. Such axial resolution is crucial for accurate segmentation and reconstruction of fine structures in biological tissues. However, in reality, the milling thickness is not always uniform across the sample surface, resulting in the axial plane looking distorted. Existing image processing approaches often: (i) assume constant section thickness; (ii) consist of multiple separate processing steps (i.e., not in an end-to-end fashion); (iii) require ground truth images for modeling, which may entail significant labor and be unsuitable for rapid analysis. ResultsWe develop a deep learning method to correct non-uniform milling artifacts observed in FIB-SEM images. The proposed method is an image-to-image translation technique that can mitigate image distortions in an unsupervised manner. It conducts cross-plane learning within 3D image volumes without any ground truth annotations. We demonstrate the efficacy of our method on a real-world micro-wasp dataset, showcasing significantly improved image quality after correction with qualitative and quantitative analysis. Contactpfister@seas.harvard.edu, jingpengw@lglab.ac.cn
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- GCTransNet: 3D mitochondrial instance segmentation based on Global Context Vision Transformers 95%
- Automated methods for 3D Segmentation of Focused Ion Beam-Scanning Electron Microscopic Images 93%
- Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information 93%
Similar papers in this journal
- Cellular porosity in dentin exhibits complex network characteristics with spatio-temporal fluctuations 95%
- FalseColor-Python: a rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital pathology 94%
- Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures 94%
Similar papers in this journal
- HD2Net: A Deep Learning Framework for Simultaneous Denoising and Deaberration in Fluorescence Microscopy 97%
- Deep Learning-driven Automatic Nuclei Segmentation of Label-free Live Cell Chromatin-sensitive Partial Wave Spectroscopic Microscopy Imaging 96%
- Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.