PDE-Constrained Optimization for Neural Image Segmentation with Physics Priors

TL;DR

This paper introduces a PDE-constrained optimization framework that incorporates physics-based priors into deep neural networks for microscopy image segmentation, improving accuracy, stability, and generalization especially with limited data.

Contribution

It presents a novel integration of reaction-diffusion and phase-field priors into deep learning models via variational regularization for improved microscopy image segmentation.

Findings

Enhanced segmentation accuracy and boundary fidelity.

Improved stability and generalization in low-sample regimes.

Demonstrated benefits of physics priors over unconstrained models.

Abstract

Segmentation of microscopy images constitutes an ill-posed inverse problem due to measurement noise, weak object boundaries, and limited labeled data. Although deep neural networks provide flexible nonparametric estimators, unconstrained empirical risk minimization often leads to unstable solutions and poor generalization. In this work, image segmentation is formulated as a PDE-constrained optimization problem that integrates physically motivated priors into deep learning models through variational regularization. The proposed framework minimizes a composite objective function consisting of a data fidelity term and penalty terms derived from reaction-diffusion equations and phase-field interface energies, all implemented as differentiable residual losses. Experiments are conducted on the LIVECell dataset, a high-quality, manually annotated collection of phase-contrast microscopy images. Training is performed on two cell types, while evaluation is carried out on a distinct, unseen cell type to assess generalization. A UNet architecture is used as the unconstrained baseline model. Experimental results demonstrate consistent improvements in segmentation accuracy and boundary fidelity compared to unconstrained deep learning baselines. Moreover, the PDE-regularized models exhibit enhanced stability and improved generalization in low-sample regimes, highlighting the advantages of incorporating structured priors. The proposed approach illustrates how PDE-constrained optimization can strengthen data-driven learning frameworks, providing a principled bridge between variational methods, statistical learning, and scientific machine learning.