Semantic Hierarchy Guided Registration Networks for Intra-subject Pulmonary CT Image Alignment

Liyun Chen, Xiaohuan Cao, Lei Chen, Yaozong Gao, Dinggang Shen, Qian Wang, Zhong Xue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

CT scanning has been widely used for diagnosis, staging and follow-up studies of pulmonary nodules, where image registration plays an essential role in follow-up assessment of CT images. However, it is challenging to align subtle structures in the lung CTs often with large deformation. Unsupervised learning-based registration methods, optimized according to the image similarity metrics, become popular in recent years due to their efficiency and robustness. In this work, we consider segmented tissues, i.e., airways, lobules, and pulmonary vessel structures, in a hierarchical way and propose a multi-stage registration workflow to predict deformation fields. The proposed workflow consists of two registration networks. The first network is the label alignment network, used to align the given segmentations. The second network is the vessel alignment network, used to further predict deformation fields to register vessels in lungs. By combining these two networks, we can register lung CT images not only in the semantic level but also in the texture level. In experiments, we evaluated the proposed algorithm on lung CT images for clinical follow-ups. The results indicate that our method has better performance especially in aligning critical structures such as airways and vessel branches in the lung, compared to the existing methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages181-189
Number of pages9
ISBN (Print)9783030597153
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 42020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period20/10/420/10/8

Keywords

  • Convolution neural network
  • Lung CT follow-up
  • Medical image registration

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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