用隐空间流模型提升医学体积图像分割的不确定性建模效率
Latent-to-Latent Flow for Volumetric Stochastic Segmentation
- 在图像和标签的隐空间间构建流模型，实现随机分割
- 效率提升最高达14倍，且保持临床可用性能
- 适合放射治疗规划与多器官分割等需要不确定性的场景
医学图像分割中的观察者间变异性带来的不确定性对治疗方案制定至关重要。该领域研究受限于大规模医学数据集缺乏多重标注，尤其是体积数据还面临尺度与计算复杂性挑战。流匹配已成为生成建模的强大框架，并在处理图像隐表示时表现出色。本文提出一种隐空间到隐空间的流方法，通过编码图像与标签空间的表示，实现医学体积的随机分割。我们在两个挑战性任务上评估该方法：放疗规划中的边界不确定性建模和多器官结构分割。结果表明，相比全分辨率模型，效率最高提升14倍，同时保持临床相关性能。
原文摘要 · Abstract (English)
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.
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