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Authors: Masato Ito and Fumihiko Ino

Affiliation: Osaka University, Japan

ISBN: 978-989-758-278-3

Keyword(s): Image Registration, Nonrigid Registration, Deep Learning, Training Data.

Related Ontology Subjects/Areas/Topics: Bioimaging ; Biomedical Engineering ; Feature Recognition and Extraction Methods ; Image Processing Methods

Abstract: In this paper, we propose an automated method for generating training sets required for realizing deep learning based image registration. The proposed method minimizes effort for supervised learning by automatically generating thousands of training sets from a small number of seed sets, i.e., tens of deformation vector fields obtained with a conventional registration method. To automate this procedure, we solve an inverse problem instead of a direct problem; we produce a floating image by applying a deformation vector fieldFto a reference image and let the inverse vector of F be the ground truth for these images. In experiments, the proposed method took 33 minutes to produce 169,890 training sets from approximately 670,000 2-D magnetic resonance (MR) images and 30 seed sets. We further trained GoogLeNet with these training sets and performed holdout validation to compare the proposed method with the conventional registration method in terms of recall and precision. As a resul t, the proposed method increased recall and precision from 50% to 80%, demonstrating the impact of deep learning for image registration problems. (More)

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Paper citation in several formats:
Ito, M. and Ino, F. (2018). An Automated Method for Generating Training Sets for Deep Learning based Image Registration.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING, ISBN 978-989-758-278-3, pages 140-147. DOI: 10.5220/0006634501400147

@conference{bioimaging18,
author={Masato Ito. and Fumihiko Ino.},
title={An Automated Method for Generating Training Sets for Deep Learning based Image Registration},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING,},
year={2018},
pages={140-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006634501400147},
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: BIOIMAGING,
TI - An Automated Method for Generating Training Sets for Deep Learning based Image Registration
SN - 978-989-758-278-3
AU - Ito, M.
AU - Ino, F.
PY - 2018
SP - 140
EP - 147
DO - 10.5220/0006634501400147

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