Medical image segmentation is a crucial task that relies on the ability to
accurately identify and isolate regions of interest in medical images. Thereby,
generative approaches allow to capture the statistical properties of
segmentation masks that are dependent on the respective structures. In this
work we propose FlowSDF, an image-guided conditional flow matching framework to
represent the signed distance function (SDF) leading to an implicit
distribution of segmentation masks. The advantage of leveraging the SDF is a
more natural distortion when compared to that of binary masks. By learning a
vector field that is directly related to the probability path of a conditional
distribution of SDFs, we can accurately sample from the distribution of
segmentation masks, allowing for the evaluation of statistical quantities.
Thus, this probabilistic representation allows for the generation of
uncertainty maps represented by the variance, which can aid in further analysis
and enhance the predictive robustness. We qualitatively and quantitatively
illustrate competitive performance of the proposed method on a public nuclei
and gland segmentation data set, highlighting its utility in medical image
segmentation applications.

提出了一种基于图像引导的 SDF 条件流匹配框架，用于表示分割掩模的隐式分布，并通过采样分割掩模的分布来评估统计量，从而生成方差表示的不确定性地图，从而提高医学图像分割的预测鲁棒性和分析能力。