L2L-Flow moves volumetric stochastic segmentation into latent space: about 14x faster radiotherapy-target inference with a competitive GED of 0.161
The work introduces Latent-to-Latent Flow (L2L-Flow), which first compresses labels into a latent space with a volumetric label autoencoder and then learns a rectified flow between an image-conditional latent prior and frozen latent label representations, yielding stochastic segmentation on a private radiotherapy clinical-target-volume dataset (55 cases, 5-fold cross-validation) and the CURVAS multi-organ dataset (90 cases), with inference about 14x faster than full-resolution Flow-SSN (0.936 s/image versus 15.296 s/image) at a GED of 0.161, alongside a time-shifted noise schedule that improves Flow-SSN stability on high-dimensional volumetric data.