Latent-to-Latent Flow for Volumetric Stochastic Segmentation

2026-09-07

AI Summary

研究通过引入一种潜在到潜在流技术解决医学图像分割中的不确定性问题，提高了处理效率并保持了临床相关性能。

Abstract

Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in this area is inhibited by the lack of multiple annotations for large-scale medical datasets, especially for volumetric data, which suffers from additional scaling and computational complexity challenges. Flow matching has emerged as a powerful framework for generative modelling and has also been demonstrated to maintain strong performance when working with latent representations of images. In this work, we introduce a latent-to-latent flow technique for stochastic segmentation of medical volumes via encoded representations of both the image and label space. We evaluate our method on two challenging applications covering delineation uncertainty for radiotherapy planning and multiple organ structure segmentation, improving efficiency up to 14x compared with full resolution models while maintaining clinically relevant performance.

Problem

• uncertainty
• medical image segmentation
• volumetric data
• inter-observer variability

Innovation

• latent-to-latent flow
• volumetric stochastic segmentation
• encoded representations