Vision foundation models (e.g., SAM2, CLIP) show strong generalization in natural image analysis but degrade significantly in specialized domains like medical imaging. This is critical for tasks such as brain tumor segmentation, where errors directly affect surgical planning and patient outcomes. In such contexts, segmentation must be highly reliable and structurally precise, underscoring the need for adaptable methods with low error tolerance. While fine-tuning is the dominant strategy, it is computationally expensive and prone to forgetting. To address this, we propose CausalBridgeNet, a causality-guided correction framework for medical image segmentation. Inspired by predictive coding theories of the Bayesian brain, our method introduces a Predictive Causal Reasoning Unit (PCRU) that estimates structured error maps and delivers targeted feedback to iteratively refine predictions. This forms a closed-loop, error-aware correction mechanism without modifying the foundation model. By keeping the backbone frozen, CausalBridgeNet preserves general visual priors while enhancing task-specific accuracy. On the BraTS 2025 benchmark, it achieves an average Dice score of 84.48 and HD95 of 5.48 across tumor subregions, demonstrating its effectiveness for high-precision medical segmentation.

Three-dimensional medical image segmentation plays a significant role in clinical diagnosis, treatment planning, and disease research, as it provides doctors with precise anatomical and lesion information and improves the accuracy and efficiency of medical decision-making. However, most existing 3D segmentation approaches rely heavily on densely volumetric data and often fail to perform segmentation properly for incomplete 3D volume acquisition, i.e., missing slices. In this work, we present InterFrameNet, a framework designed to predict intermediate lesion structures by modeling spatial relationships across frames, enabling robust segmentation performance under sparse acquisition conditions, without requiring full-volume information. Our method explicitly models cross-frame spatial continuity and leverages structural relationships between available frames to accurately infer missing lesion regions. This design significantly reduces the dependence on consecutive frames while fully exploiting contextual anatomical information. Extensive experiments on brain lesion datasets demonstrate that our approach achieves robust segmentation performance under sparse acquisition settings, offering a practical solution to maximize usability of incomplete clinical imaging data.

Accurate segmentation of lesions is crucial for disease diagnosis and treatment planning. However, blurring and low contrast in the imaging process can affect segmentation results. We have observed that noninvasive medical imaging shares considerable similarities with natural images under low light conditions and that nocturnal animals possess extremely strong night vision capabilities. Inspired by the dark vision of these nocturnal animals, we proposed a novel plug-and-play dark vision network (DVNet) to enhance the model's perception for low-contrast medical images. Specifically, by employing the wavelet transform, we decompose medical images into subbands of varying frequencies, mimicking the sensitivity of photoreceptor cells to different light intensities. To simulate the antagonistic receptive fields of horizontal cells and bipolar cells, we design a Mamba-Enhanced Fusion Module to achieve global information correlation and enhance contrast between lesions and surrounding healthy tissues. Extensive experiments demonstrate that the DVNet achieves SOTA performance in various medical image segmentation tasks.