2024/12/13 by Talon Chandler, Ivan E. Ivanov, Chandler, Talon +51 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Optics (physics.optics) #Quantitative Methods (q-bio.QM) #cs.CV #physics.optics #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2412.09775
openalex publication_date 2024/12/13 · arxiv published 2024/12/13 · openalex created_date 2025/10/10 · arxiv updated 2025/12/20 · openalex updated_date 2026/07/28
Correlative computational microscopy can accelerate imaging and modeling of cellular dynamics by relaxing trade-offs inherent to dynamic imaging. Existing computational microscopy frameworks are either specialized or overly generic, limiting use to fixed configurations or domain experts. We introduce WaveOrder, a generalist wave-optical framework for imaging the architectural order of biomolecules. WaveOrder reconstructs diverse specimen properties from multi-channel acquisitions, with or without fluorescence. It provides a unified representation of linear optical properties and differentiable physics-based image formation models spanning widefield, confocal, light-sheet, and oblique label-free geometries. WaveOrder uses physics-informed ML to auto-tune model parameters and solve blind shift-variant restoration problems. This open-source, PyTorch-based framework enables scalable quantitative imaging across scales from organelles to adult zebrafish, and improves restoration of cellular structures in high-throughput experiments. We validate WaveOrder on diverse imaging applications, demonstrating its ability to recover biomolecular structure beyond the limits of existing approaches.