loading
Documents

Research.Publish.Connect.

Paper

Authors: J. M. Sloan 1 ; K. A. Goatman 2 and J. P. Siebert 3

Affiliations: 1 Toshiba Medical Visualisation Services, Europe Ltd. and University of Glasgow, United Kingdom ; 2 Toshiba Medical Visualisation Services and Europe Ltd., United Kingdom ; 3 University of Glasgow, United Kingdom

ISBN: 978-989-758-278-3

Keyword(s): Rigid Registration, Deep Learning, Mono-modality, Multi-modality, Magnetic Resonance Imaging, Inverse Consistency.

Related Ontology Subjects/Areas/Topics: Bioimaging ; Biomedical Engineering ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Health Engineering and Technology Applications ; Image Processing Methods ; Magnetic Resonance Imaging ; NeuroSensing and Diagnosis ; Neurotechnology, Electronics and Informatics

Abstract: Many traditional computer vision tasks, such as segmentation, have seen large step-changes in accuracy and/or speed with the application of Convolutional Neural Networks (CNNs). Image registration, the alignment of two or more images to a common space, is a fundamental step in many medical imaging workflows. In this paper we investigate whether these techniques can also bring tangible benefits to the registration task. We describe and evaluate the use of convolutional neural networks (CNNs) for both mono- and multi- modality registration and compare their performance to more traditional schemes, namely multi-scale, iterative registration. This paper also investigates incorporating inverse consistency of the learned spatial transformations to impose additional constraints on the network during training and investigate any benefit in accuracy during detection. The approaches are validated with a series of artificial mono-modal registration tasks utilizing T1-weighted MR brain i mages from the Open Access Series of Imaging Studies (OASIS) study and IXI brain development dataset and a series of real multi-modality registration tasks using T1-weighted and T2-weighted MR brain images from the 2015 Ischemia Stroke Lesion segmentation (ISLES) challenge. The results demonstrate that CNNs give excellent performance for both mono- and multi- modality head and neck registration compared to the baseline method with significantly fewer outliers and lower mean errors. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 100.26.182.28

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Sloan, J.; Goatman, K. and Siebert, J. (2018). Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-278-3, pages 89-99. DOI: 10.5220/0006543700890099

@conference{bioimaging18,
author={J. M. Sloan. and K. A. Goatman. and J. P. Siebert.},
title={Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,},
year={2018},
pages={89-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006543700890099},
isbn={978-989-758-278-3},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,
TI - Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration
SN - 978-989-758-278-3
AU - Sloan, J.
AU - Goatman, K.
AU - Siebert, J.
PY - 2018
SP - 89
EP - 99
DO - 10.5220/0006543700890099

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.