Mitigating Over-Saturated Fluorescence Images Through a Semi-Supervised Generative Adversarial Network
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024, 2024, pp. 1-5
- Issue Date:
- 2024-05
Recently Added
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1750876.pdf | Published version | 1.57 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is new to OPUS and is not currently available.
Multiplex immunofluorescence MxIF imaging is a critical tool in biomedical research offering detailed insights into cell composition and spatial context As an example DAPI staining identifies cell nuclei while CD20 staining helps segment cell membranes in MxIF However a persistent challenge in MxIF is saturation artifacts which hinder single cell level analysis in areas with over saturated pixels Traditional gamma correction methods for fixing saturation are limited often incorrectly assuming uniform distribution of saturation which is rarely the case in practice This paper introduces a novel approach to correct saturation artifacts from a data driven perspective We introduce a two stage high resolution hybrid generative adversarial network HD mixGAN which merges unpaired CycleGAN and paired pix2pixHD network architectures This approach is designed to capitalize on the available small scale paired data and the more extensive unpaired data from costly MxIF data Specifically we generate pseudo paired data from large scale unpaired over saturated datasets with a CycleGAN and train a Pix2pixGAN using both small scale real and large scale synthetic data derived from multiple DAPI staining rounds in MxIF This method was validated against various base lines in a downstream nuclei detection task improving the F1 score by 6 over the baseline This is to our knowledge the first focused effort to address multi round saturation in MxIF images offering a specialized solution for enhancing cell analysis accuracy through improved image quality The source code and implementation of the proposed method are available at https github com MASILab DAPIArtifactRemoval git
Please use this identifier to cite or link to this item: