Abstract
Automatic nasopharyngeal carcinoma (NPC) segmentation in magnetic resonance (MR) images remains challenging since NPC is infiltrative and typically has a small or even tiny volume, making it indiscernible from tightly connected surrounding tissues. Recent methods using deep learning models performed unsatisfactorily since the boundary between NPC and its neighbor tissues is difficult to distinguish. In this paper, a novel Convolutional Neural Network (CNN) with recurrent attention modules (RAMs) is proposed to tackle the problem. To enhance the performance of NPC segmentation, the proposed fully automatic NPC segmentation method with recurrent attention exploits the semantic features in higher layers to guide the learning of features in lower layers. Features are fed into RAMs iteratively from the higher layers to the lower ones. The lower layers are updated iteratively by the guidance of higher layers to render with discriminative capability. Our proposed method was validated in a dataset including 596 patients, experimental results demonstrate that our method outperforms state-of-the-art methods.
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Acknowledgments
This work was supported by grants from the National Natural Science Foundation of China (no. 61771007, no. 81572652), Health & Medical Collaborative Innovation Project of Guangzhou City, China (grants 201604020003, 201803010021), Science and Technology Planning Projects of Guangdong Province (2016A010101013, 2017B020226004), and the Fundamental Research Fund for the Central Universities (2017ZD051).
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Huang, Jb., Zhuo, E., Li, H., Liu, L., Cai, H., Ou, Y. (2019). Achieving Accurate Segmentation of Nasopharyngeal Carcinoma in MR Images Through Recurrent Attention. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11768. Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_55
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DOI: https://doi.org/10.1007/978-3-030-32254-0_55
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