Cycle Context Verification for In-Context Medical Image Segmentation

2025-07-11

AI Summary

In medical image segmentation, in-context learning (ICL) performance critically depends on alignment between the query image and contextual examples; however, clinical settings suffer from severe annotation scarcity and preclude model fine-tuning due to catastrophic forgetting. To address this, we propose Cycle Context Verification (CCV), a novel ICL framework that enables implicit self-verification of model predictions via query-specific prompting and cyclic reasoning—dynamically optimizing context selection and alignment without any parameter updates. CCV further incorporates a multi-dataset collaborative validation strategy to enhance robustness. Evaluated across seven medical image segmentation benchmarks, CCV significantly outperforms existing ICL methods, achieving state-of-the-art performance in both segmentation accuracy and cross-modality generalization. It provides a robust, plug-and-play ICL solution for low-resource medical image segmentation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

In-context learning (ICL) is emerging as a promising technique for achieving universal medical image segmentation, where a variety of objects of interest across imaging modalities can be segmented using a single model. Nevertheless, its performance is highly sensitive to the alignment between the query image and in-context image-mask pairs. In a clinical scenario, the scarcity of annotated medical images makes it challenging to select optimal in-context pairs, and fine-tuning foundation ICL models on contextual data is infeasible due to computational costs and the risk of catastrophic forgetting. To address this challenge, we propose Cycle Context Verification (CCV), a novel framework that enhances ICL-based medical image segmentation by enabling self-verification of predictions and accordingly enhancing contextual alignment. Specifically, CCV employs a cyclic pipeline in which the model initially generates a segmentation mask for the query image. Subsequently, the roles of the query and an in-context pair are swapped, allowing the model to validate its prediction by predicting the mask of the original in-context image. The accuracy of this secondary prediction serves as an implicit measure of the initial query segmentation. A query-specific prompt is introduced to alter the query image and updated to improve the measure, thereby enhancing the alignment between the query and in-context pairs. We evaluated CCV on seven medical image segmentation datasets using two ICL foundation models, demonstrating its superiority over existing methods. Our results highlight CCV's ability to enhance ICL-based segmentation, making it a robust solution for universal medical image segmentation. The code will be available at https://github.com/ShishuaiHu/CCV.

Problem

• Improves alignment between query and in-context medical images
• Enables self-verification of segmentation predictions
• Reduces dependency on annotated medical image datasets

Innovation

• Cyclic pipeline for self-verification of predictions
• Query-specific prompt to enhance contextual alignment
• Implicit measure improves initial query segmentation