Structure-wise Uncertainty for Curvilinear Image Segmentation

Published: 29 Nov 2023, Last Modified: 29 Nov 2023NeurReps 2023 PosterEveryoneRevisionsBibTeX
Submission Track: Extended Abstract
Keywords: Topological Representation, Discrete Morse Theory, Structural Uncertainty, Image Segmentation, Curvilinear Structures
TL;DR: We propose a novel Probabilistic DMT to model the inherent uncertainty within each structure (intra-structural uncertainty) by sampling its representations via a perturb-and-walk scheme.
Abstract: Segmenting curvilinear structures like blood vessels and roads poses significant challenges due to their intricate geometry and weak signals. To expedite large-scale annotation, it is essential to adopt semi-automatic methods such as proofreading by human experts. In this abstract, we focus on estimating uncertainty for such tasks, so that highly uncertain, and thus error-prone structures can be identified for human annotators to verify. Unlike prior work that generates pixel-wise uncertainty maps, we believe it is essential to measure uncertainty in the units of topological structures, e.g., small pieces of connections and branches. To realize this, we employ tools from topological data analysis, specifically discrete Morse theory (DMT), to first extract the structures and then reason about their uncertainties. On multiple 2D and 3D datasets, our methodology generates superior structure-wise uncertainty maps compared to existing models.
Submission Number: 35