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


Hu, Ruxue; Wang, Hongkai; Ristaniemi, Tapani; Zhu, Wentao; Sun, Xiaobang (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, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1368-1371. DOI: 10.1109/EMBC44109.2020.9176363


JYU authors or editors


Publication details

All authors or editors: Hu, Ruxue; Wang, Hongkai; Ristaniemi, Tapani; Zhu, Wentao; Sun, Xiaobang

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

Place and date of conference: Montreal, QC, Canada, 20.-24.7.2020

ISBN: 978-1-7281-1991-5

eISBN: 978-1-7281-1990-8

Journal or series: Annual International Conference of the IEEE Engineering in Medicine and Biology Society

ISSN: 2375-7477

eISSN: 1557-170X

Publication year: 2020

Pages range: 1368-1371

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: http://doi.org/10.1109/EMBC44109.2020.9176363

Open Access: Publication channel is not openly available


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.


Keywords: computed tomography; imaging; lungs; machine learning; neural networks (information technology)

Free keywords: lung CT image; neural networks


Contributing organizations


Ministry reporting: Yes

Preliminary JUFO rating: 1


Last updated on 2020-01-09 at 10:59