SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance.

• The SeqSeg addresses the problem of background dominance to achieve accurate NPC detection and segmentation. • Intelligent deep Q-learning-based nasopharyngeal carcinoma detection model is proposed. • Experiments on a large dataset from multi-device and multi-center demonstrate the reliability of the proposed method. • Efficient reward function and exploration strategy boost NPC detectio performance. • Recurrent attention and dilated border weighted loss function foster NPC segmentation. [Display omitted] 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. [ABSTRACT FROM AUTHOR]