Big Model and Small Model : Remote modeling and local information extraction module for medical image segmentation
Lianghui Xu, Liejun Wang, Yongming Li, Anyu Du
In the past few years, convolutional neural networks (CNN) and Transformer have achieved great success in medical image segmentation, but they each have inevitable drawbacks. Among them, convolution operation is difficult to calculate the relationship between elements at a certain position and elements far away from the position in the feature map. However, Transformer tends to ignore the importance of local information when exploring the correlation between overall elements. In order to allow the network to acquire both the ability to explore local details and compute the correlation between distant elements, this paper proposes TransUNet++ based on TransUNet. Specifically, this paper proposes two modules, Big Model and Small Model, to explore the element relationship between feature maps. Among them, on the basis of the whole feature map as the basic unit, the Big model can not only calculate the correlation between distant elements in the feature map but also extract the detailed information of the local feature map. On the basis of taking 1/4 of the feature map as the basic unit, the Small model not only explores the correlation between distant elements but also extracts the local details of the feature map. We demonstrate on the Synapse multi-organ segmentation dataset(Synapse) and Automated cardiac diagnosis challenge dataset (ACDC) that using either the Big Model or the Small Model alone can improve the experimental results, and using the Big Model and the Small Model in parallel can achieve more optimal experimental results. Among them, in Synapse dataset, we achieved 80.87% dice score and 24.79% HD score, and in ACDC dataset, we achieved 91.41% dice score and 1.08% HD score.