Modified Hybrid Task Cascade for Lung Nodules Segmentation in CT Images with Guided Anchoring

Shihuai Xu, Huijuan Lu, Minchao Ye, Ke Yan, Wenjie Zhu, Qun Jin

研究成果

抄録

As lung cancer continues to threaten human health, Computer-Aided Diagnostic (CAD) plays an increasingly significant role in lung cancer diagnosis, and convolutional neural networks (CNNs) have shown the outstanding performance in image segmentation. In this work, Hybrid Task Cascade (HTC) is used to segment lung nodules that are difficult to find in CT images. Considering that lung nodules are usually quite small, this study integrates Feature Pyramid Network (FPN) into ResNet-50 to make full use of multi-scale feature and improve the segmentation accuracy of small target nodules. In addition, given that existing defects in Region Proposal Network (RPN), which refers to most of generated anchors are irrelevant to target objects, and the conventional method are unaware of the shapes of target objects, this work proposes to use Guided Anchoring to replace RPN in HTC and generate anchors more effectively. Experimental results on the LIDC-IDRI dataset demonstrate that the modified HTC improves the segmentation accuracy of lung nodules.

本文言語English
ホスト出版物のタイトルProceedings of the 2020 12th International Conference on Machine Learning and Computing, ICMLC 2020
出版社Association for Computing Machinery
ページ433-438
ページ数6
ISBN(電子版)9781450376426
DOI
出版ステータスPublished - 2020 2 15
イベント12th International Conference on Machine Learning and Computing, ICMLC 2020 - Shenzhen, China
継続期間: 2020 2 152020 2 17

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference12th International Conference on Machine Learning and Computing, ICMLC 2020
国/地域China
CityShenzhen
Period20/2/1520/2/17

ASJC Scopus subject areas

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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