A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Hu, Ruxue; Wang, Hongkai; Ristaniemi, Tapani; Zhu, Wentao; Sun, Xiaobang

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

Konferenssin paikka ja aika: Montreal, QC, Canada,  20.-24.7.2020

ISBN: 978-1-7281-1991-5

eISBN: 978-1-7281-1990-8

Lehti tai sarja: Annual International Conference of the IEEE Engineering in Medicine and Biology Society

ISSN: 2375-7477

eISSN: 1557-170X

Julkaisuvuosi: 2020

Artikkelin sivunumerot: 1368-1371

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Tiivistelmä

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.

YSO-asiasanat: tietokonetomografia; kuvantaminen; keuhkot; koneoppiminen; neuroverkot

Vapaat asiasanat: lung CT image; neural networks

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1