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Title: Deep Learning Based Lung Region Segmentation with Data Preprocessing by Generative Adversarial Nets
Authors: Nitta, Jumpei
Nakao, Megumi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-5508-4366 (unconfirmed)
Imanishi, Keiho
Matsuda, Tetsuya  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-2339-1521 (unconfirmed)
Author's alias: 新田, 潤平
中尾, 恵
松田, 哲也
Keywords: Lung
Surgery
Image segmentation
Strain
Computed tomography
Data preprocessing
Biomedical imaging
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Journal title: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Start page: 1278
End page: 1281
Thesis number: 9176214
Abstract: In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve safety. For organs that do not deform much during surgery, preoperative computed tomography (CT) images can be used to understand their three-dimensional structure, however, deformation estimation is necessary for organs that deform substantially. Even though the intraoperative deformation estimation of organs has been widely studied, two-dimensional organ region segmentations from camera images are necessary to perform this estimation. In this paper, we propose a region segmentation method using U-net for the lung, which is an organ that deforms substantially during surgery. Because the accuracy of the results for smoker lungs is lower than that for non-smoker lungs, we improved the accuracy by translating the texture of the lung surface using a CycleGAN.
Description: [2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 20-24 July 2020, Montreal, QC, Canada]
Rights: © 2020 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.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/265387
DOI(Published Version): 10.1109/EMBC44109.2020.9176214
PubMed ID: 33018221
Appears in Collections:Journal Articles

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