A Channel-Region Adaptive Unet for Lung Inflammation Segmentation

Accurate lung inflammation segmentation is essential for clinical decision-making, yet remains challenging due to the large variability in lesion appearance and location across different lung regions. Existing CNN-based models excel at local feature extraction, but they struggle to capture long-range dependencies and complex spatial relationships, such as those between the left and right lung lobes. Transformer-based models, while effective in modeling long-range dependencies, incur high computational costs and often fail to capture irregular anatomical relationships due to their reliance on Euclidean positional encodings. To overcome these challenges, we propose a novel Channel-Region Adaptive Unet (CRA-Unet) for accurate lung inflammation segmentation. Specifically, we design a Channel-Region Adaptive (CRA) layer that expands the recalibration process of the Squeeze-Excitation layer to include not only the channel dimension but also the height and width dimensions, enabling dynamical element-wise feature adjustment within different regions of interest across all three dimensions—channel, height, and width. Additionally, we propose a region-adaptive positional encoding strategy that learns dynamic weights for spatial locations, allowing the model to capture both intra-region and inter-region spatial relationships. Unlike traditional Euclidean positional encodings, which assume regular and grid-like spatial structures, our strategy can adapt to the irregular and asymmetric spatial relationships commonly found in anatomical structures such as the lungs. Experimental results on several datasets demonstrate that our CRA-Unet achieves state-of-the-art segmentation performance while maintaining high computational efficiency.