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

2025-11-27

AI Summary

To address insufficient segmentation accuracy in 3D microscopic images and the low efficiency caused by heavy reliance on manual correction, this paper proposes an uncertainty-guided human-in-the-loop correction framework. It introduces voxel-wise uncertainty maps—used for the first time—to direct user attention to high-risk regions, enabling precise and efficient manual intervention. Integrating 3D deep learning, Bayesian uncertainty estimation, and interactive visualization, we develop VessQC, an open-source tool. A user study on real biological data demonstrates that error detection recall significantly improves from 67% to 94.0% (p = 0.007), without a statistically significant increase in total correction time. This work bridges the gap between model outputs and human expert needs, establishing a new paradigm for high-fidelity, scalable 3D bioimage analysis.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Develops an open-source tool for uncertainty-guided curation of 3D microscopy segmentations
• Addresses error-prone results from state-of-the-art 3D segmentation models
• Bridges the gap between automated uncertainty estimation and practical human-computer interaction

Innovation

• Uncertainty-guided curation tool for 3D microscopy segmentation
• Integrates uncertainty maps to highlight error-prone regions
• Enables efficient human-in-the-loop refinement of volumetric segmentations