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

Additional URL

https://doi.org/10.1016/j.media.2022.102381

Share

COinS