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arXiv · 2511.08046

ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation

Aya Elgebaly·Nikolaos Delopoulos·Juliane Hörner-Rieber·Carolin Rippke·Sebastian Klüter·Luca Boldrini·Lorenzo Placidi·Riccardo Dal Bello·Nicolaus Andratschke·Michael Baumgartl·Claus Belka·Christopher Kurz·Guillaume Landry·Shadi Albarqouni
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Abstract

Automated medical image segmentation suffers from high inter-observer variability, particularly in tasks such as lung nodule delineation, where experts often disagree. Existing approaches either collapse this variability into a consensus mask or rely on separate model branches for each annotator. We introduce ProSona, a two-stage framework that learns a continuous latent space of annotation styles, enabling controllable personalization via natural language prompts. A probabilistic U-Net backbone captures diverse expert hypotheses, while a prompt-guided projection mechanism navigates this latent space to generate personalized segmentations. A multi-level contrastive objective aligns textual and visual representations, promoting disentangled and interpretable expert styles. Across the LIDC-IDRI lung nodule and multi-institutional prostate MRI datasets, ProSona reduces the Generalized Energy Distance by 17% and improves mean Dice by more than one point compared with DPersona. These results demonstrate that natural-language prompts can provide flexible, accurate, and interpretable control over personalized medical image segmentation. Our implementation is available online 1 .

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Aya Elgebaly, Nikolaos Delopoulos, Juliane Hörner-Rieber, Carolin Rippke, Sebastian Klüter, Luca Boldrini, Lorenzo Placidi, Riccardo Dal Bello, Nicolaus Andratschke, Michael Baumgartl, Claus Belka, Christopher Kurz, Guillaume Landry, Shadi Albarqouni. 2025-11-11. ProSona: Prompt-Guided Personalization for Multi-Expert Medical Image Segmentation. https://arxiv.org/abs/2511.08046

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