MCRformer: Morphological Constraint Reticular Transformer for 3D Medical Image Segmentation

Medical image segmentation is essential in medical image analysis since it can provide reliable assistance in computer-aided clinical diagnosis, treatment planning, and intervention. Although deep learning algorithms based on CNNs and Transformers have made notable progress in medical image segmentation, it is still challenging owing to the objects with complex structures, low discrimination and differences between individuals. To alleviate these issues, we propose a novel 3D medical image segmentation network based on Transformers and CNNs combining morphological information and reticular mechanism. Firstly, the morphological constraint stream is designed to learn the prior shape information based on the CNN model and make the attention maps focus on target tasks for enhancing the interpretability. Secondly, the Reticular Transformer is utilized to obtain multi-scale information based on the Transformer, which can bind the local texture information and underlying semantic information to further acquire the feature maps with sufficient details and receptive field. The experiments demonstrate that our proposed method outperforms many existing segmentation models in terms of the performance in metrics DSC and HD (80.46% in DSC on the Synapse dataset and 90.83% in DSC on the ACDC dataset). The code will be released at https://github.com/rocklijun/MCRformer.