Linear and Deformable Image Registration with 3D Convolutional Neural Networks

Christodoulidis, Stergios; Sahasrabudhe, Mihir; Vakalopoulou, Maria; Chassagnon, Guillaume; Revel, Marie-Pierre; Mougiakakou, Stavroula Georgia; Paragios, Nikos (September 2018). Linear and Deformable Image Registration with 3D Convolutional Neural Networks. In: 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018). Lecture Notes in Computer Science: Vol. 11040 (pp. 13-22). Springer, Cham 10.1007/978-3-030-00946-5_2

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Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a novel convolutional neural network architecture that couples linear and deformable registration within a unified architecture endowed with near real-time performance. Our framework is modular with respect to the global transformation component, as well as with respect to the similarity function while it guarantees smooth displacement fields. We evaluate the performance of our network on the challenging problem of MRI lung registration, and demonstrate superior performance with respect to state of the art elastic registration methods. The proposed deformation (between inspiration & expiration) was considered within a clinically relevant task of interstitial lung disease (ILD) classification and showed promising results.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Christodoulidis, Stergios and Mougiakakou, Stavroula Georgia

Subjects:

600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISBN:

978-3-030-00946-5

Series:

Lecture Notes in Computer Science

Publisher:

Springer, Cham

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

20 Sep 2018 08:31

Last Modified:

20 Sep 2018 08:31

Publisher DOI:

10.1007/978-3-030-00946-5_2

ArXiv ID:

1809.06226

URI:

https://boris.unibe.ch/id/eprint/120026

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