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Title: Reconstructing 3d lung shape from a single 2d image during the deaeration deformation process using model-based data augmentation
Authors: Wu, Shuqiong
Nakao, Megumi  kyouindb  KAKEN_id  orcid (unconfirmed)
Tokuno, Junko
Chen-Yoshikawa, Toyofumi
Matsuda, Tetsuya
Author's alias: 武, 淑瓊
中尾, 恵
陳, 豊史
松田, 哲也
Keywords: CNN
deaeration deformation
machine learning
data augmentation
3D shape reconstruction
Issue Date: May-2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Journal title: 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
Thesis number: 8834454
Abstract: Three-dimensional (3D) shape reconstruction is particularly important for computer assisted medical systems, especially in the case of lung surgeries, where large deaeration deformation occurs. Recently, 3D reconstruction methods based on machine learning techniques have achieved considerable success in computer vision. However, it is difficult to apply these approaches to the medical field, because the collection of a massive amount of clinic data for training is impractical. To solve this problem, this paper proposes a novel 3D shape reconstruction method that adopts both data augmentation techniques and convolutional neural networks. In the proposed method, a deformable statistical model of the 3D lungs is designed to augment various training data. As the experimental results demonstrate, even with a small database, the proposed method can realize 3D shape reconstruction for lungs during a deaeration deformation process from only one captured 2D image. Moreover, the proposed data augmentation technique can also be used in other fields where the training data are insufficient.
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The full-text file will be made open to the public on 1 May 2021 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version.
DOI(Published Version): 10.1109/BHI.2019.8834454
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