Pitfalls of Topology-Aware Image Segmentation
MCML Authors
Martin Menten
Dr.
JRG Leader AI for Vision
Daniel Rückert
Prof. Dr.
Director
Abstract
Martin Menten
Dr.
JRG Leader AI for Vision
Daniel Rückert
Prof. Dr.
Director
Abstract
Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuron or vessel segmentation. Despite the recent surge in topology-aware methods addressing this challenge, their real-world applicability is hindered by flawed benchmarking practices. In this paper, we identify critical pitfalls in model evaluation that include inadequate connectivity choices, overlooked topological artifacts in ground truth annotations, and inappropriate use of evaluation metrics. Through detailed empirical analysis, we uncover these issues' profound impact on the evaluation and ranking of segmentation methods. Drawing from our findings, we propose a set of actionable recommendations to establish fair and robust evaluation standards for topology-aware medical image segmentation methods.
inproceedings
IPMI 2025
Information Processing in Medical Imaging. Kos Island, Greece, May 25-30, 2025.Authors
A. H. Berger • L. Lux • A. Weers • M. J. Menten • D. Rückert • J. C. PaetzoldLinks
DOIResearch Area
BibTeXKey: BLW+25