# Disentangling Prompt Dependence to Evaluate Segmentation Reliability in Gynecological MRI

arXiv: https://arxiv.org/abs/2603.13369 · HTML: https://mlsanity.com/paper/2603.13369/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM](https://mlsanity.com/paper/2409.00924/) (2024)
- [AoP-SAM: Automation of Prompts for Efficient Segmentation](https://mlsanity.com/paper/2505.11980/) (2025)
- [Curriculum Prompting Foundation Models for Medical Image Segmentation](https://mlsanity.com/paper/2409.00695/) (2024)
- [UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model](https://mlsanity.com/paper/2505.05049/) (2025)
- [Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation](https://mlsanity.com/paper/2503.23507/) (2025)

## Abstract (arXiv)

Promptable segmentation models (e.g., the Segment Anything Models) enable generalizable, zero-shot segmentation across diverse domains. Although predictions are deterministic for a fixed image-prompt pair, the robustness of these models to variations in user prompts, referred to as prompt dependence, remains underexplored. In safety-critical workflows with substantial inter-user variability, interpretable and informative frameworks are needed to evaluate prompt dependence. In this work, we assess the reliability of promptable segmentation by analyzing and measuring its sensitivity to prompt variability. We introduce the first formulation of prompt dependence that explicitly disentangles prompt ambiguity (inter-user variability) from local sensitivity (interaction imprecision), offering an interpretable view of segmentation robustness. Experiments on two female pelvic MRI datasets for uterus and bladder segmentation reveal a strong negative correlation between both metrics and segmentation performance, highlighting the value of our framework for assessing robustness. The two metrics have low mutual correlation, supporting the disentangled design of our formulation, and provide meaningful indicators of prompt-related failure modes.