  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.
