Publication Type
Journal Article
Version
publishedVersion
Publication Date
2-2022
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.(c) 2022 Elsevier B.V. All rights reserved.
Keywords
Nasopharyngeal carcinoma, Background dominance, NPC Detection and segmentation, Deep Q-learning, Recurrent attention
Discipline
Information Security
Research Areas
Information Systems and Management
Publication
Medical Image Analysis
Volume
78
ISSN
1361-8415
Identifier
10.1016/j.media.2022.102381
Publisher
Elsevier
Citation
TAO, Guihua; LI, Haojiang; HUANG, Jiabin; HAN, Chu; CHEN, Jiazhou; RUAN, Guangying; HUANG, Wenjie; HU, Yu; DAN, Tingting; ZHANG, Bin; and Shengfeng HE.
SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance. (2022). Medical Image Analysis. 78,.
Available at: https://ink.library.smu.edu.sg/sis_research/7840
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.media.2022.102381