Flow Stochastic Segmentation Networks
Fabio De Sousa Ribeiro, Omar Todd, Charles Jones, Avinash Kori, Raghav Mehta, Ben Glocker
Abstract
We propose the Flow Stochastic Segmentation Network (Flow-SSN), a generative model for probabilistic segmentation featuring discrete-time autoregressive and modern continuous-time flow parameterisations. We prove fundamental limitations of the low-rank parameterisation of previous methods and show that Flow-SSNs can estimate arbitrarily high-rank pixel-wise covariances without assuming the rank or storing the distributional parameters. Flow-SSNs are also more efficient to sample from than standard diffusion-based segmentation models, as most of the model capacity is allocated to learning the base distribution of the flow, which constitutes an expressive prior. We apply Flow-SSNs to challenging medical imaging benchmarks and achieve state-of-the-art results.
BibTeX
@InProceedings{De_Sousa_Ribeiro_2025_ICCV,
author = {De Sousa Ribeiro, Fabio and Todd, Omar and Jones, Charles and Kori, Avinash and Mehta, Raghav and Glocker, Ben},
title = {Flow Stochastic Segmentation Networks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14754-14765}
}