Coarse-to-fine Nasopharyngeal Carcinoma Segmentation in MRI via Multi-stage Rendering

摘要

Accurate nasopharyngeal carcinoma (NPC) segmentation in magnetic resonance image (MRI) is crucial for diagnosis and treatment. However, most existing deep learning methods performed unsatisfactorily, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable boundary, making it indiscernible from tightly connected surrounding tissue in immense and complex background. To address the background dominant problem, this paper proposes a coarse-to-fine deep model. The proposed model starts with predicting a coarse mask with a well-designed segmentation module, followed by a boundary rendering module, which exploits semantic information from different layers of feature maps to refine the boundary of the coarse mask. The designed rendering module is shown to achieve superior performance with dramatically fewer parameters by operating only on the segmented mask, rather than on the whole feature maps as the popular methods do. Extensive experiments are conducted on a collected dataset consisting of 2000 MRI slices from 596 patients. Experimental results demonstrate that the proposed model not only outperforms six popular segmentation models but also has a considerable generalization capability on existing models.