A 2.5D multi-path fusion network framework with focusing on z-axis 3D joint for medical image segmentation

Accurate segmentation of organs and tumors from medical images is to diagnose and treat diseases more accurately. Many organs, such as the representative pancreas and spleen, have blurred boundaries, small size, large variance, and inter-class imbalance. 2.5D methods based on 2D networks are currently widely used in various areas of medical image segmentation. However, 2D network-based methods are still unable to truly balance the contextual residual difference information. Therefore, we propose a new 2.5D Multi-path Transformer fusion network(MTr-Net) that combines Z-axis information from 3D networks to address organs and tumors boundary deformation, while balancing contextual residual information. A fusion of two different methods is proposed to further refine the segmentation results of organs and tumors. Our method is evaluated on five widely accepted public datasets of pancreas and tumor, spleen, kidney and tumor, and skin lesions, which contain different imaging modalities. Specifically, these include the NIH public dataset, the MSD Pancreas public dataset, the MSD Spleen dataset, the KiTS19 dataset and the ISIC2018 dataset. The algorithm performs well on all the above datasets. Our source codes will be released at https://github.com/graduation37289/MTr-Net, once the manuscript is accepted for publication.