Multi-organ segmentation using TransUNet with improved multi-dimensional multi-layer perceptron

Multi-organ segmentation from abdominal images is an important task. Due to the imbalance between different organ and the differences in size, shape, and contrast of different organs, it is a challenging problem in the field of medical image analysis. A powerful feature extraction model is the key to solving this challenge. TransUNet is a popular solution. MultiLayer Perceptron (MLP) is its important part. However, the existing MLP modules only operate on a single feature dimension without interaction across different dimensions. This paper proposes an improved MLP module with multidimensional interaction, named SMLP-Mixer. The SMLP-Mixer achieves multidimensional information interaction while possessing lower computational complexity and fewer parameters in the feature dimension, which can reduce the risk of overfitting. Experiments on the Synapse dataset demonstrate its effectiveness. Compared to TransUNet, the Dice coefficient exhibits a 1.98% improvement, and the HD95 (mm) shows a significant enhancement of 10.83.