MSA²-Net: Utilizing self-adaptive convolution module to extract multi-scale information in medical image segmentation

The nnU-Net framework effectively automates hyperparameter selection; however, its fixed internal configurations-notably convolution kernel sizes-restrict its flexibility. This limitation is pronounced in 3D medical imaging, where anatomical structures undergo continuous spatial evolution along the Z-axis. In this study, we introduce a self-adaptive convolution module designed to dynamically tune the effective receptive field, matching the dynamic structural transformations of organs. By employing a differentiable soft-attention mechanism to aggregate candidate kernels, the network adaptively optimizes its scale sensitivity. This integration allows MSA²-Net to capture both global context and local nuances within feature maps. The module is strategically embedded into two core components: the multi-scale convolution bridge and the multi-scale amalgamation decoder. In the Bridge, it refines CSWin Transformer outputs by aligning features with the inherent spatial continuity of volumetric data, thereby mitigating redundancies that might otherwise hinder decoding. Simultaneously, the multi-scale amalgamation decoder leverages this module to precisely reconstruct organ details as their size and shape fluctuate across slices. This mechanism ensures the decoder preserves seamless topological intricacies within the feature maps, yielding superior segmentation accuracy. Leveraging this architecture, MSA²-Net achieves competitive Dice scores of 86.49%, 92.56%, 93.37%, and 92.98% on the Synapse, ACDC, Kvasir, and ISIC2017 datasets, respectively. Extensive experiments validate the model's robustness in handling complex spatial variations across diverse medical modalities.