Localizing Active Dynamics in Scientific Images: Physics-Grounded Visual Explanations for Segmentation Models

Scientific image segmentation identifies structures of interest, but it does not necessarily reveal where the underlying physical or biological mechanism that produced them is currently active. We use active dynamics to denote image regions in which the governing mechanism is currently changing the observed system. These regions differ from the segmentation target. For example, a mask may identify material that has already crystallized, while the remaining transformation occurs in the untransformed regions between crystals. This creates a structural limitation for gradient-based explanation: it attributes the segmentation prediction to supporting image evidence, hence inherently drawn toward the segmented structure, even when the governing dynamics are active in a different region. We introduce PGAD-X (Physics-Grounded Active-Dynamics eXplanation), a framework that produces an active-dynamics explanation alongside the segmentation mask. PGAD-X encodes the governing law and its characteristic spatial scale to condition shared visual features, and uses attention-based explanation to localize where the corresponding dynamics are expressed. Our key design decouples the explanation from the segmentation gradient while grounding both outputs in the same visual representation, enabling the model to localize physically active regions, rather than merely restate the predicted mask. A lightweight second stage aligns the explanation with automatically derived physics references while keeping the segmentation backbone fixed. Across three microscopy datasets, three governing mechanisms, and three segmentation backbones, PGAD-X improves active dynamics localization over gradient-based and latent-alignment baselines while preserving or improving segmentation accuracy. We release the physics references and evaluation protocol.