MRI Image Segmentation of Nasopharyngeal Carcinoma Using Multi-Scale Cascaded Fully Convolutional Network

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck, and its incidence is the highest all around the world. Intensive radiotherapy using computer-aided diagnosis is the best technique for the treatment of NPC. The key step of radiotherapy is the delineation of the target areas and organs at risk, that is, tumor images segmentation. We proposed the segmentation method of NPC image based on multi-scale cascaded fully convolutional network. It used cascaded network and multi-scale feature for a coarse-to-fine segmentation to improve the segmentation effect. In coarse segmentation, image blocks and data augmentation were used to compensate for the shortage of training samples. In fine segmentation, Atrous Spatial Pyramid Pooling (ASPP) was used to increase the receptive field and image feature transfer, which was added in the Dense block of DenseNet. In the process of upsampling, the features of multiple views were fused to reduce false positive samples. Additionally, in order to improve the class imbalance problem, Focal Loss was used to weight the loss function of tumor voxel distance because it could reduce the weight of background category samples. The cascaded network can alleviate the problem of gradient disappearance and obtain a smoother boundary. The experimental results were quantitatively analyzed by DSC, ASSD and F1_score values, and the results showed that the proposed method was effective for nasopharyngeal carcinoma segmentation compared with other methods in this paper.