Bridging 3D Deep Learning and Curation for Analysis and High-Quality Segmentation in Practice

Simon Püttmann; Jonathan Jair Sànchez Contreras; Lennart Kowitz; Peter Lampen; Saumya Gupta; Davide Panzeri; Nina Hagemann; Qiaojie Xiong; Dirk M. Hermann; Cao Chen; Jianxu Chen

arXiv · 2511.22236

Bridging 3D Deep Learning and Curation for Analysis and High-Quality Segmentation in Practice

Simon Püttmann·Jonathan Jair Sànchez Contreras·Lennart Kowitz·Peter Lampen·Saumya Gupta·Davide Panzeri·Nina Hagemann·Qiaojie Xiong·Dirk M. Hermann·Cao Chen·Jianxu Chen

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Abstract

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.

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Simon Püttmann, Jonathan Jair Sànchez Contreras, Lennart Kowitz, Peter Lampen, Saumya Gupta, Davide Panzeri, Nina Hagemann, Qiaojie Xiong, Dirk M. Hermann, Cao Chen, Jianxu Chen. 2025-11-27. Bridging 3D Deep Learning and Curation for Analysis and High-Quality Segmentation in Practice. https://arxiv.org/abs/2511.22236

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