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

TL;DR

This paper introduces an advanced deep learning approach combining multiscale, attention mechanisms, and ensemble methods to improve the accuracy and reliability of automatic GTV segmentation in nasopharynx cancer radiotherapy planning.

Contribution

It proposes a novel 2.5D CNN with spatial and channel attention modules, multi-scale training, and ensemble techniques for more accurate and robust GTV segmentation from CT images.

Findings

Improved segmentation accuracy over existing methods.

Enhanced robustness through multi-model ensemble.

Reliable uncertainty estimation for clinical use.

Abstract

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.