WaveOrder: A differentiable wave-optical framework for scalable biological microscopy with diverse modalities

TL;DR

WaveOrder is a versatile, physics-informed deep learning framework that enhances biological microscopy by enabling scalable, multi-modal imaging and accurate reconstruction of cellular structures across various microscopy techniques.

Contribution

We introduce WaveOrder, a generalist, differentiable wave-optical framework that unifies diverse microscopy modalities and improves biomolecular structure reconstruction using physics-informed machine learning.

Findings

Successfully reconstructs cellular structures beyond existing limits.

Enables scalable imaging from organelles to whole organisms.

Improves high-throughput cellular imaging accuracy.

Abstract

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.