  Radiotherapy is the main treatment modality for nasopharynx cancer.
Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI
images is a prerequisite for radiotherapy. As manual delineation is
time-consuming and laborious, automatic segmentation of GTV has a potential to
improve this process. Currently, most of the deep learning-based automatic
delineation methods of GTV are mainly performed on medical images like CT
images. However, it is challenged by the low contrast between the pathology
regions and surrounding soft tissues, small target region, and anisotropic
resolution of clinical CT images. To deal with these problems, we propose a
2.5D Convolutional Neural Network (CNN) to handle the difference of inplane and
through-plane resolution. Furthermore, we propose a spatial attention module to
enable the network to focus on small target, and use channel attention to
further improve the segmentation performance. Moreover, we use multi-scale
sampling method for training so that the networks can learn features at
different scales, which are combined with a multi-model ensemble method to
improve the robustness of segmentation results. We also estimate the
uncertainty of segmentation results based on our model ensemble, which is of
great importance for indicating the reliability of automatic segmentation
results for radiotherapy planning.
