Abstract
Accurate liver and tumor segmentation from CT images is essential for cancer diagnosis, treatment planning, and response assessment. However, manual segmentation is labor-intensive and variable, while standard automated models lack the flexibility to adapt to diverse clinical needs or inherent image uncertainties. To bridge this gap, we introduce User-Preference Alignment with Uncertainty-Aware Interactive Rectification (UAIR), a novel framework designed for efficient and adaptive segmentation. Instead of requiring laborious pixel-level corrections, UAIR presents the clinician with a small, curated set of diverse segmentation candidates generated by quantifying model uncertainty. The user simply selects the most suitable option, allowing the framework to iteratively refine its results and align with specific clinical preferences. This selection-based approach drastically reduces the human interaction cost. We validated UAIR on a large-scale, multi-center CT dataset, demonstrating superior accuracy (DSC 0.776) over existing manual positional prompting (DSC 0.685) and less prompting efforts. UAIR provides a clinically-viable solution that integrates seamless human guidance, enabling rapid and robust segmentation for downstream quantitative analysis.
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Data availability
All imaging datasets analyzed in this study are publicly accessible. The AbdomenAtlas dataset is available at (https://github.com/MrGiovanni/AbdomenAtlas). Processed or derived data supporting the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
This work was supported by the Natural Science Foundation of Hubei Province (Grant No. 2025AFD774). The authors would also like to express their sincere gratitude to Yuchuan Jiang for his valuable contributions to the field, from which this study has greatly benefited. His previously published work provided important insights and methodological guidance for the present research.
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G.Z., C.G., and Y.W. contributed equally to this work, having full access to all study data and assuming responsibility for the integrity and accuracy of the analyses (Validation and Formal analysis). G.Z., C.G., and G.H. conceptualized the study, designed the methodology, and participated in securing research funding (Conceptualization, Methodology, and Funding acquisition). Y.W. and X.Z. carried out data acquisition, curation, and investigation (Investigation, Data curation) and provided key resources, instruments, and technical support (Resources and Software). Z.W. drafted the initial manuscript and generated visualizations (Writing—Original Draft and Visualization). T.C. and B.Y. supervised the project, coordinated collaborations, and ensured administrative support (Supervision and Project administration). All authors contributed to reviewing and revising the manuscript critically for important intellectual content (Writing—Review & Editing) and approved the final version for submission.
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Zhao, G., Wang, Y., Gong, C. et al. User-preference alignment with uncertainty-aware interactive rectification for liver organ and tumor segmentation and analysis from CT images. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02544-2
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DOI: https://doi.org/10.1038/s41746-026-02544-2
