Bridging 3D Deep Learning and Curation for Analysis and High-Quality Segmentation in Practice
This addresses the need for efficient, high-quality segmentation in bioimage analysis, though it is incremental as it builds on existing uncertainty estimation methods.
The paper tackles the problem of error-prone 3D microscopy image segmentation by developing VessQC, an open-source tool that uses uncertainty maps to guide manual curation, improving error detection recall from 67% to 94.0% without significantly increasing curation time.
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.