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.
With the development of Transformer and its derivatives over the last two years, several research have integrated Transformer with CNN or in instead of CNN to advance medical image segmentation. Although they often create acceptable feature maps for large organs, segmentation accuracy for small organs is less than satisfactory. Transformer excels at global context, but it displays limits in capturing fine-grained features, especially in medical areas, because convolution is unable to describe long-term correlations prevalent in images. This is because local information modeling lacks a spatial inductive bias. Currently available Transformer-based segmentation networks are not often optimized for this issue, we therefore use transformer as the backbone and provide a medical image segmentation network with deformation attention. The model uses attention mechanism to boost the impact of feature maps to address the issue of low accuracy of tiny organ segmentation in multi-organ segmentation tasks. On the Synapse dataset, our model has so far produced SOTA results.
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