Learning Continuous Morphological Trajectories via Latent Principal Curves
The paper introduces MorphCurveVAE, a two-stage deep learning framework that utilizes a multi-branch variational auto-encoder and topologically-aware principal curves to reconstruct continuous, biologically plausible 3D morphological trajectories from static microscopy snapshots, effectively modeling dynamic cellular processes like mitosis without requiring time-resolved data.