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Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping
Saurabh Joshi1,2, André Forjaz1,2, Kyu Sang Han1,2
1Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD.
Biorxiv : the Preprint Server for Biology
|March 18, 2024
Summary
Generative AI using Frame Interpolation for Large Image Motion (FILM) enhances 3D biological imaging by improving resolution and reducing artifacts. This method synthesizes skipped images, decreasing imaging time and preserving crucial biological details across diverse datasets.
Area of Science:
- 3D Biological Imaging
- Computational Biology
- Artificial Intelligence in Microscopy
Background:
- Novel imaging platforms generate large 3D datasets, enabling complex spatial analysis.
- Image quality limitations (resolution, missing data, artifacts) hinder quantitative accuracy.
- Traditional interpolation methods struggle to preserve fine biological details and image quality.
Purpose of the Study:
- To adapt Frame Interpolation for Large Image Motion (FILM), a generative AI model, for spatial interpolation in 3D biological imaging.
- To evaluate FILM's performance against traditional linear interpolation for enhancing image quality and preserving biological information.
- To demonstrate FILM's utility in reducing imaging time and repairing image artifacts.
Main Methods:
- Application of FILM, a generative AI model, for spatial interpolation of 3D biological image data.
- Comparative analysis of FILM against linear interpolation using various imaging modalities, species, tissues, and resolutions.
- Assessment of image quality metrics (contrast, variance, luminance) and preservation of microanatomical features and cell counts.
Main Results:
- FILM significantly outperforms linear interpolation in producing functional synthetic images.
- FILM effectively preserves biological information, including microanatomical features and cell counts.
- FILM repairs tissue damage, reduces stitching artifacts, and can decrease imaging time by synthesizing skipped frames.
- The method demonstrates versatility across diverse imaging modalities, species, tissues, and resolutions.
Conclusions:
- Generative AI, specifically FILM, offers a powerful approach to enhance the resolution, quality, and throughput of 3D biological imaging datasets.
- FILM's ability to interpolate spatial data improves quantitative analysis and reduces imaging constraints.
- This AI-driven method holds significant potential for advancing biological discovery through improved 3D imaging.
