用不确定性引导自动优化提示，提升医学图像分割精度。
MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM
- 基于不确定性评估自动优化多提示输入，增强模型鲁棒性
- 在五种医学影像数据集上平均提升1.7%至20.5%
- 适合需要高可靠性分割的临床辅助诊断场景
医学图像分割通用模型MedSAM表现出色，但对提示类型和位置敏感。本文提出MedSAM-U，一种不确定性引导的自动多提示自适应框架，以提升分割可靠性。首先训练集成多提示适配器的MPA-MedSAM，以适应多样提示输入；随后利用不确定性引导机制评估提示及其初始分割结果的置信度；进一步应用新型不确定性引导提示优化技术，自动生成可靠提示与分割结果。在多模态数据集上验证，相较于MedSAM，MedSAM-U在五个不同模态数据集上实现平均1.7%至20.5%的性能提升。
原文摘要 · Abstract (English)
The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field. However, its sensitivity to varying prompt types and locations poses challenges. This paper addresses these challenges by focusing on the development of reliable prompts that enhance MedSAM's accuracy. We introduce MedSAM-U, an uncertainty-guided framework designed to automatically refine multi-prompt inputs for more reliable and precise medical image segmentation. Specifically, we first train a Multi-Prompt Adapter integrated with MedSAM, creating MPA-MedSAM, to adapt to diverse multi-prompt inputs. We then employ uncertainty-guided multi-prompt to effectively estimate the uncertainties associated with the prompts and their initial segmentation results. In particular, a novel uncertainty-guided prompts adaptation technique is then applied automatically to derive reliable prompts and their corresponding segmentation outcomes. We validate MedSAM-U using datasets from multiple modalities to train a universal image segmentation model. Compared to MedSAM, experimental results on five distinct modal datasets demonstrate that the proposed MedSAM-U achieves an average performance improvement of 1.7\% to 20.5\% across uncertainty-guided prompts.
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