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Radiotherapy is the main treatment modality for nasopharynx cancer.Delineation of Gross Target Volume (GTV) from medical images such as CT and MRIimages is a prerequisite for radiotherapy. As manual delineation istime-consuming and laborious, automatic segmentation of GTV has a potential toimprove this process. Currently, most of the deep learning-based automaticdelineation methods of GTV are mainly performed on medical images like CTimages. However, it is challenged by the low contrast between the pathologyregions and surrounding soft tissues, small target region, and anisotropicresolution of clinical CT images. To deal with these problems, we propose a2.5D Convolutional Neural Network (CNN) to handle the difference of inplane andthrough-plane resolution. Furthermore, we propose a spatial attention module toenable the network to focus on small target, and use channel attention tofurther improve the segmentation performance. Moreover, we use multi-scalesampling method for training so that the networks can learn features atdifferent scales, which are combined with a multi-model ensemble method toimprove the robustness of segmentation results. We also estimate theuncertainty of segmentation results based on our model ensemble, which is ofgreat importance for indicating the reliability of automatic segmentationresults for radiotherapy planning.

Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention