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

arXiv: https://arxiv.org/abs/2511.22236 · HTML: https://mlsanity.com/paper/2511.22236/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [Topology-Aware Uncertainty for Image Segmentation](https://mlsanity.com/paper/2306.05671/) (2023)
- [vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation](https://mlsanity.com/paper/2411.17386/) (2024)
- [3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations](https://mlsanity.com/paper/2302.03299/) (2023)
- [SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images](https://mlsanity.com/paper/2411.07601/) (2024)
- [Uncertainty-guided annotation enhances segmentation with the human-in-the-loop](https://mlsanity.com/paper/2404.07208/) (2024)

## Abstract (arXiv)

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.