Transformer-based heart organ segmentation using a novel axial attention and fusion mechanism

Recent research on transformer-based models have highlighted particular methods for medical image segmentation. Additionally, the majority of transformer-based network designs used in computer vision applications have a significant number of parameters and demand extensive training datasets. Inspired by the success of transformers in recent researches, the unet-transformer approach has become one of the de-facto ideas in overcoming the above challenges. In this manuscript, a novel unet-transformer approach was proposed for heart image segmentation to solve parameters, limited dataset, over segmentation, sensitivity noise and higher training time problems. A framework in which a novel width and height wise axial attention mechanism is incorporated into the design to effectively give positional embeddings and encode spatial flattening. Furthermore, a novel local and global spatial attention mechanism is proposed to effectively learn the local and global interactions between encoder features. Finally, we introduce a mechanism to fuse both contexts for better feature representation and preparation into the decoder. The results demonstrate that our prototype provides a robust novel axial-attention mechanism.