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Cambridge Image Analysis Seminars

Generative Medical Image Segmentation Using Distance Transforms

With Lea Bogensperger, TU Graz

Generative Medical Image Segmentation Using Distance Transforms

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. We employ two different generative modeling frameworks to represent the signed distance function (SDF) leading to an implicit distribution of segmentation masks: a conditional score-based generative model and an image-guided conditional
flow matching model. 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.

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