MultiTrans: Multi-scale feature fusion transformer with transfer learning strategy for multiple organs segmentation of head and neck CT images

Abstract

Radiotherapy with precise segmentation of head and neck organs at risk (OARs) is one of the important treatment methods for head and neck cancer. In routine clinical practice, OARs are manually segmented by doctors to avoid irreversible adverse reactions caused by radiotherapy, which is time-consuming and laborious. To assist doctors in OARs segmentation, a MultiTrans framework with a multi-scale feature fusion module was proposed in this paper. In the multi-scale feature fusion module, the original image and the feature map of CNN were fused together to form a compound feature map for more complete high-resolution global information. In addition, the global information was also fully utilized in MultiTrans by using the feature map restored from the compound feature map in the skip connection. The multi-scale interactive high-resolution information can make full use of medical image information and obtain features more comprehensively, thus improve the segmentation accuracy. Experiments showed that MultiTrans had an average Dice score coefficient (DSC) of 74.01% in all organs, effectively improved segmentation accuracy. In addition, we proposed a transfer learning strategy for small organs by transferring the weight parameters of organs with a large amount of data to organs with a small amount of data to speed up the convergence of MultiTrans and reduce the demand for data volume in the MultiTrans. With this strategy, the average DSC of small organs was obviously increased, making the segmentation of small organs more accurate. The proposed framework and transfer learning strategy have the potential of assisting doctors in OARs delineation.