A4 Article in conference proceedings
Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network (2020)


Hu, R., Wang, H., Ristaniemi, T., Zhu, W., & Sun, X. (2020). Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network. In EMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1368-1371). IEEE. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/EMBC44109.2020.9176363


JYU authors or editors


Publication details

All authors or editorsHu, Ruxue; Wang, Hongkai; Ristaniemi, Tapani; Zhu, Wentao; Sun, Xiaobang

Parent publicationEMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Place and date of conferenceMontreal, QC, Canada 20.-24.7.2020

ISBN978-1-7281-1991-5

eISBN978-1-7281-1990-8

Journal or seriesAnnual International Conference of the IEEE Engineering in Medicine and Biology Society

ISSN2375-7477

eISSN1557-170X

Publication year2020

Pages range1368-1371

PublisherIEEE

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/EMBC44109.2020.9176363

Publication open accessNot open

Publication channel open access


Abstract

Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However, most of them learned the deformation field through intensity similarity but ignored the importance of aligning anatomical landmarks (e.g., the branch points of airway and vessels). Accurate alignment of anatomical landmarks is essential for obtaining anatomically correct registration. In this work, we propose landmark constrained learning with a convolutional neural network (CNN) for lung CT registration. Experimental results of 40 lung 3D CT images show that our method achieves 0.93 in terms of Dice index and 3.54 mm of landmark Euclidean distance on lung CT registration task, which outperforms state-of-the-art methods in registration accuracy.


Keywordscomputed tomographyimaginglungsmachine learningneural networks (information technology)

Free keywordslung CT image; neural networks


Contributing organizations


Ministry reportingYes

Reporting Year2020

JUFO rating1


Last updated on 2024-23-02 at 19:31