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In medical image processing, accurate 3D segmentation is a crucial prerequisite for effective computer-aided diagnosis. Recently, UNet-based network architectures have become the mainstream choice for medical image segmentation. However, the inherent geometric limitations of traditional convolutions restrict their ability to perceive irregularly shaped lesions, and existing attention mechanisms often overlook positional information, which is essential for models to understand the configuration of target regions. To address these challenges, we propose an encoder-decoder network that integrates shape perception with 3D localization to generate precise segmentation masks. Central to our framework is the Deformable Coordinate Kernel Attention (DCK-Attention) module, which adaptively deforms the sampling grid by learning features from the input image, thereby enhancing attention and perception of regions of interest through spatial localization information. We validate the effectiveness of our method on the Brain Tumor Segmentation (BraTS) and Automated Cardiac Diagnosis Challenge (ACDC) datasets. Experimental results demonstrate that our approach outperforms existing segmentation methods, offering a significant advancement and providing a referenceable solution for the field of medical image segmentation.
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