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.

Jia-Bin Huang, Enhong Zhuo, Haojiang Li, Lizhi Liu, Hongmin Cai, Yangming Ou: Achieving Accurate Segmentation of Nasopharyngeal Carcinoma in MR Images Through RecurrentAttention. MICCAI (5) 2019: 494-502

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Publication Details

Date of publication:
October 10, 2019
Medical Image Computing and Computer-Assisted Intervention
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