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

TL;DR

ProSona introduces a novel framework that uses natural language prompts to personalize medical image segmentation, effectively capturing expert variability and improving segmentation accuracy across datasets.

Contribution

It presents a two-stage, prompt-guided approach with a continuous latent space for personalized segmentation, advancing interpretability and flexibility in medical image analysis.

Findings

Reduces Generalized Energy Distance by 17%

Improves mean Dice score by over 1 point

Demonstrates effective personalization across datasets

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 .