Multi-Scale Global-Instance Prompt Tuning for Continual Test-Time Adaptation in Medical Image Segmentation

Distribution shift is a common challenge in medical images obtained from different clinical centers, significantly hindering the deployment of pre-trained semantic segmentation models in real-world applications across multiple domains. Continual Test-Time Adaptation (CTTA) has emerged as a promising approach to address cross-domain distribution shifts during continually evolving target domains. Most existing CTTA methods rely on incrementally updating model parameters, which inevitably suffer from error accumulation and catastrophic forgetting, especially in long-term adaptation. Recent prompt-tuning-based works have shown potential to mitigate the two issues above by updating only visual prompts. While these approaches have demonstrated promising performance, several limitations remain: 1) lacking multi-scale prompt diversity, 2) inadequate incorporation of instance-specific knowledge, and 3) risk of privacy leakage. To overcome these limitations, we propose Multi-scale Global-Instance Prompt Tuning (MGIPT), to enhance scale diversity of prompts as well as capture both globaland instance-level knowledge for robust CTTA. Specifically, MGIPT consists of an Adaptive-scale Instance Prompt (AIP) and a Multi-scale Global-level Prompt (MGP). AIP dynamically learns lightweight and instance-specific prompts to mitigate error accumulation with adaptive optimal-scale selection mechanism. MGP captures domain-level knowledge across different scales to ensure robust adaptation with anti-forgetting capabilities. These complementary components are combined through a weighted ensemble approach, enabling effective dual-level adaptation that integrates both global and local information. Extensive experiments on medical image segmentation benchmarks (five optic disc/cup datasets and four polyp datasets) demonstrate that our MGIPT outperforms state-of-the-art methods, achieving robust adaptation across continually changing target domains. Notably, our MGIPT exhibits particularly strong performance in longterm CTTA scenarios, showing great anti-forgetting ability.