User-preference alignment with uncertainty-aware interactive rectification for liver organ and tumor segmentation and analysis from CT images
npj Digital Medicine, Published online: 03 April 2026; doi:10.1038/s41746-026-02544-2 User-preference alignment with uncertainty-aware interactive rectification for liver organ and tumor segmentation and analysis from CT images
References
- Wild, C. P., Weiderpass, E. & Stewart, B. W. World cancer report (IARC, 2020).
- Sedano, R. et al. Immunotherapy for cancer: common gastrointestinal, liver, and pancreatic side effects and their management. Am. J. Gastroenterol. 117, 1917–1932 (2022).
Google Scholar
- Midya, A. et al. Computerized diagnosis of liver tumors from ct scans using a deep neural network approach. IEEE J. Biomed. Health Inform. 27, 2456–2464 (2023).
Google Scholar
- Shiina, S. et al. Percutaneous ablation for hepatocellular carcinoma: comparison of various ablation techniques and surgery. Can. J. Gastroenterol. Hepatol. 2018, 4756147 (2018).
Google Scholar
- Gul, S. et al. Deep learning techniques for liver and liver tumor segmentation: a review. Comput. Biol. Med. 147, 105620 (2022).
Google Scholar
- Bilic, P. et al. The liver tumor segmentation benchmark (lits). Med. Image Anal. 84, 102680 (2023).
Google Scholar
- Ma, J. et al. Abdomenct-1k: Is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. 44, 6695–6714 (2022).
Google Scholar
- Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised recist guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).
Google Scholar
- Virdis, F. et al. Clinical outcomes of primary arterial embolization in severe hepatic trauma: a systematic review. Diagn. Interv. Imaging 100, 65–75 (2019).
Google Scholar
- Todorov, M. I. et al. Machine learning analysis of whole mouse brain vasculature. Nat. Methods 17, 442–449 (2020).
Google Scholar
- Azad, R. et al. Medical image segmentation review: the success of U-Net. IEEE Trans. Pattern Anal. Mach. Intell. 46, 10076–10095 (2024).
Google Scholar
- Alirr, O. I. & Rahni, A. A. A. Survey on liver tumour resection planning system: steps, techniques, and parameters. J. Digit. Imaging 33, 304–323 (2020).
Google Scholar
- Moghbel, M., Mashohor, S., Mahmud, R. & Saripan, M. I. B. Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography. Artif. Intell. Rev. 50, 497–537 (2018).
Google Scholar
- Hesamian, M. H., Jia, W., He, X. & Kennedy, P. Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32, 582–596 (2019).
Google Scholar
- Baumgartner, C. F. et al. Phiseg: Capturing uncertainty in medical image segmentation. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, 119–127 (Springer, 2019).
- Prasanna, P. G. et al. Normal tissue protection for improving radiotherapy: where are the gaps? Transl. Cancer Res. 1, 35 (2012).
Google Scholar
- Zhu, J., Wu, J., Ouyang, C., Kamnitsas, K. & Noble, J. A. Spa: efficient user-preference alignment against uncertainty in medical image segmentation. In Proc. IEEE/CVF International Conference on Computer Vision, 23731–23740 (IEEE, 2025).
- Zhang, Y., Shen, Z. & Jiao, R. Segment anything model for medical image segmentation: current applications and future directions. Comput. Biol. Med. 171, 108238 (2024).
Google Scholar
- Kirillov, A. et al. Segment anything. In Proc. IEEE/CVF International Conference on Computer Vision, 4015–4026 (IEEE, 2023).
- Ma, J. et al. Segment anything in medical images. Nat. Commun. 15, 654 (2024).
Google Scholar
- Wang, H. et al. Sam-med3d: towards general-purpose segmentation models for volumetric medical images. In Proc. European Conference on Computer Vision, 51–67 (Springer, 2024).
- Zhang, Y. et al. Enhancing the reliability of auto-prompting sam for medical image segmentation with uncertainty estimation and rectification. In Proc. IEEE/CVF International Conference on Computer Vision, 1282–1291 (IEEE, 2025).
- Deng, G. et al. Sam-u: Multi-box prompts triggered uncertainty estimation for reliable sam in medical image. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, 368–377 (Springer, 2023).
- Li, W. et al. Abdomenatlas: a large-scale, detailed-annotated & multi-center dataset for efficient transfer learning and open algorithmic benchmarking. Med. Image Anal. 97, 103285 (2024).
Google Scholar
- Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. In Proc. International Conference on Medical image computing and computer-assisted intervention, 234–241 (Springer, 2015).
- Chen, J. et al. Transunet: rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med. Image Anal. 97, 103280 (2024).
Google Scholar
- Lei, W., Xu, W., Li, K., Zhang, X. & Zhang, S. Medlsam: Localize and segment anything model for 3d ct images. Med. Image Anal. 99, 103370 (2025).
Google Scholar
- Zhang, D., Chen, B., Chong, J. & Li, S. Weakly-supervised teacher-student network for liver tumor segmentation from non-enhanced images. Med. Image Anal. 70, 102005 (2021).
Google Scholar
- Ma, Y., Wang, J., Yang, J. & Wang, L. Model-heterogeneous semi-supervised federated learning for medical image segmentation. IEEE Trans. Med. Imaging 43, 1804–1815 (2024).
Google Scholar
- Jiang, M., Yang, H., Cheng, C. & Dou, Q. Iop-fl: Inside-outside personalization for federated medical image segmentation. IEEE Trans. Med. Imaging 42, 2106–2117 (2023).
Google Scholar
- Zhao, J. et al. United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast mri. Med. Image Anal. 73, 102154 (2021).
Google Scholar
- Ji, Y. et al. Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Adv. Neural Inf. Process. Syst. 35, 36722–36732 (2022).
Google Scholar
- Zhang, Y. et al. Seganypet: Universal promptable segmentation from positron emission tomography images. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), 21107–21116 (IEEE, 2025).
- Wang, Z., Wu, Z., Agarwal, D. & Sun, J. Medclip: contrastive learning from unpaired medical images and text. Proc. Conf. Empir. Methods Nat. Lang. Process. 2022, 3876 (2022).
Google Scholar
- Moor, M. et al. Med-flamingo: a multimodal medical few-shot learner. In Machine Learning for Health (ML4H), 353–367 (PMLR, 2023).
- Zhang, Y. et al. Semisam+: rethinking semi-supervised medical image segmentation in the era of foundation models. Med. Image Anal. (2025).
- Ali, M. et al. A review of the segment anything model (sam) for medical image analysis: accomplishments and perspectives. Comput. Med. Imaging Graph. 119, 102473 (2025).
Google Scholar
- Jiao, R. et al. Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation. Comput. Biol. Med. 169, 107840 (2024).
Google Scholar
- Zhang, Y., Jiao, R., Liao, Q., Li, D. & Zhang, J. Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation. Artif. Intell. Med. 138, 102476 (2023).
Google Scholar
- Zou, K. et al. A review of uncertainty estimation and its application in medical imaging. Meta Radiol. 1, 100003 (2023).
Google Scholar
- Zhou, N. et al. Medsam-u: Uncertainty-guided auto multi-prompt adaptation for reliable medsam. IEEE Trans. Circuits Syst. Video Technol. 36, 3768–3781 (2025).
Google Scholar
Download references
Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
analysisalignmentpublished
Exploring Early Web Patterns for Modern AI Agent Development
Exploring Early Web Patterns for Modern AI Agent Development The repository 6551Team/claude-code-design-guide presents an interesting thesis: visual and architectural solutions from the early web - from first HTML pages to 1990s browser interfaces - can enrich modern AI agent development using Claude Code. The Connection Isn't Forced Early internet had to solve problems similar to today's AI agency challenges: Constrained client resources Need for fast content delivery Operating under unstable connections These solutions - interface design patterns, data structures, state management approaches - were largely forgotten, though some are precisely suited for the new generation of autonomous systems. ## Practical Guide, Not Just History The project isn't merely historical reference; it's a pra

Automate Your Ingredient Alerts with AI: A Practical Guide
The Manual Labeling Headache For small-batch food producers, a supplier's silent reformulation is a regulatory nightmare. Manually tracking every spec sheet and Certificate of Analysis (COA) is slow, error-prone, and pulls you away from crafting your product. What if your system could alert you the moment an ingredient changes? The Principle: Structured Triggers Automated Workflows The core principle is moving from reactive manual checks to a proactive system of structured triggers and automated workflows . Instead of quarterly audits, you build a process where specific changes—like an added allergen—automatically create an alert and kick off a predefined action checklist. Your Central Command: The Digital Ingredient List This starts with a single source of truth: a cloud database like Air

500,000 Deepfake Identities Expose How Investigations Fall Apart in Court
Analyzing the architectural shifts required to fight synthetic identity fraud highlights a terrifying reality for anyone building computer vision (CV) pipelines: our detection models are currently losing the arms race against generative AI. When a single platform blocks 500,000 synthetic identities in six months, it’s a signal that the traditional "liveness check" is no longer a sufficient gatekeeper. For developers working in biometrics and facial comparison, this news represents a fundamental shift in how we must handle identity verification. We are moving from a world where we simply classify an image ("Is this a human face?") to a world where we must mathematically prove a relationship between two images in a way that survives forensic scrutiny. The Math of Defensibility: Beyond Classi
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.






Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!