Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching

Phi Van Nguyen, Huynh Trinh Ngoc, Minh Lam Nguyen Duy, Loc Nguyen Phu, Long Q. Tran

Keywords: Deep Learning · Segmentation · Uncertainty · Generative · Flow

Abstract:

Quantifying aleatoric uncertainty in medical image segmentation
is critical since it is a reflection of the natural variability observed
among expert annotators. A conventional approach is to model
the segmentation distribution using the generative model, but current
methods limit the expression ability of generative models. While current
diffusion-based approaches have demonstrated impressive performance in
approximating the data distribution, their inherent stochastic sampling
process and inability to model exact densities limit their effectiveness
in accurately capturing uncertainty. In contrast, our proposed method
leverages conditional flow matching, a simulation-free flow-based generative
model that learns an exact density, to produce highly accurate
segmentation results. By guiding the flow model on the input image and
sampling multiple data points, our approach synthesizes segmentation
samples whose pixel-wise variance reliably reflects the underlying data
distribution. This sampling strategy captures uncertainties in regions
with ambiguous boundaries, offering robust quantification that mirrors
inter-annotator differences. Experimental results demonstrate that our
method not only achieves competitive segmentation accuracy but also
generates uncertainty maps that provide deeper insights into the reliability
of the segmentation outcomes. The code for this paper is freely
available at https://github.com/huynhspm/Data-Uncertainty

Submission Number: 13