SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance |
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| Institution: | 1. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China;2. Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China;3. Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China;1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China;2. Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China;3. Department of Psychiatry, University of North Carolina at Chapel Hill, United States;4. Department of Computer Science, University of North Carolina at Chapel Hill, United States;5. Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, United States;6. Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, United States;1. Université Côte d’Azur, Inria, Epione project-team, Sophia Antipolis, France;2. Academic Department of Radiology, Hôpital Pitié-Salpétriére, Sorbonne Université, Assistance Publique des Hôpitaux de Paris, Paris, France |
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| Abstract: | Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets. |
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