Accurate 3D microscopy image segmentation is critical for quantitative bioimage analysis but even state-of-the-art foundation models yield error-prone results. Therefore, manual curation is still widely used for either preparing high-quality training data or fixing errors before analysis. We present VessQC, an open-source tool for uncertainty-guided curation of large 3D microscopy segmentations. By integrating uncertainty maps, VessQC directs user attention to regions most likely containing biologically meaningful errors. In a preliminary user study uncertainty-guided correction significantly improved error detection recall from 67% to 94.0% (p=0.007) without a significant increase in total curation time. VessQC thus enables efficient, human-in-the-loop refinement of volumetric segmentations and bridges a key gap in real-world applications between uncertainty estimation and practical human-computer interaction. The software is freely available at github.com/MMV-Lab/VessQC.
@article{arxiv.2511.22236,
title = {Bridging 3D Deep Learning and Curation for Analysis and High-Quality Segmentation in Practice},
author = {Simon Püttmann and Jonathan Jair Sànchez Contreras and Lennart Kowitz and Peter Lampen and Saumya Gupta and Davide Panzeri and Nina Hagemann and Qiaojie Xiong and Dirk M. Hermann and Cao Chen and Jianxu Chen},
journal= {arXiv preprint arXiv:2511.22236},
year = {2025}
}