🤖 AI Summary
This work addresses the ill-posed nature of microscopic image segmentation, which suffers from noise, weak boundaries, and scarce annotations, leading to poor generalization and unstable solutions in conventional deep learning approaches. The authors propose formulating segmentation as a PDE-constrained optimization problem, uniquely embedding physical priors—specifically reaction-diffusion dynamics and phase-field interfacial energy—as differentiable regularizers within a UNet architecture. This yields a composite objective function that jointly optimizes data fidelity and PDE residual loss. Trained end-to-end, the resulting physics-informed neural segmentation framework achieves significantly improved accuracy and boundary fidelity on the LIVECell dataset, outperforming unconstrained baselines especially in cross-cell-type generalization and few-shot scenarios. The approach effectively bridges the gap between variational methods, statistical learning, and scientific machine learning.
📝 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.