A Transformer-Based Network for Anisotropic 3D Medical Image Segmentation

Imaging anisotropy poses a critical challenge in applying deep learning models to 3D medical image analysis. Anisotropy downgrades model performance, especially when slice spacing varies significantly between training and clinical datasets. We propose a transformer-based model to tackle the anisotropy problem. It is adaptable to different levels of anisotropy and is computationally efficient. Our model outperforms baseline models in 3D lung cancer segmentation experiments.