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  1. A500 情報学部/情報学研究科・情報文化学部・情報科学研究科
  2. A500a 雑誌掲載論文
  3. 学術雑誌

Abdominal artery segmentation method from CT volumes using fully convolutional neural network

http://hdl.handle.net/2237/00031995
http://hdl.handle.net/2237/00031995
87ea8b85-4e28-4244-8053-76d6a1d0c688
名前 / ファイル ライセンス アクション
ijcars_arterysegmentation10_utf8_revised3rd_nocorrection.pdf ijcars_arterysegmentation10_utf8_revised3rd_nocorrection (4.9 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2020-04-03
タイトル
タイトル Abdominal artery segmentation method from CT volumes using fully convolutional neural network
言語 en
著者 Oda, Masahiro

× Oda, Masahiro

WEKO 97409

en Oda, Masahiro

Search repository
Roth, Holger R.

× Roth, Holger R.

WEKO 97410

en Roth, Holger R.

Search repository
Kitasaka, Takayuki

× Kitasaka, Takayuki

WEKO 97411

en Kitasaka, Takayuki

Search repository
Misawa, Kazunari

× Misawa, Kazunari

WEKO 97412

en Misawa, Kazunari

Search repository
Fujiwara, Michitaka

× Fujiwara, Michitaka

WEKO 97413

en Fujiwara, Michitaka

Search repository
Mori, Kensaku

× Mori, Kensaku

WEKO 97414

en Mori, Kensaku

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 “This is a post-peer-review, pre-copyedit version of an article published in [International Journal of Computer Assisted Radiology and Surgery]. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11548-019-02062-5”.
キーワード
主題Scheme Other
主題 Abdominal artery
キーワード
主題Scheme Other
主題 CT image
キーワード
主題Scheme Other
主題 Segmentation
キーワード
主題Scheme Other
主題 Fully convolutional network
抄録
内容記述 Purpose: The purpose of this paper is to present a fully automated abdominal artery segmentation method from a CT volume. Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment. Information about blood vessels (including arteries) can be used in patient-specific surgical planning and intra-operative navigation. Since blood vessels have large inter-patient variations in branching patterns and positions, a patient-specific blood vessel segmentation method is necessary. Even though deep learning-based segmentation methods provide good segmentation accuracy among large organs, small organs such as blood vessels are not well segmented. We propose a deep learning-based abdominal artery segmentation method from a CT volume. Because the artery is one of small organs that is difficult to segment, we introduced an original training sample generation method and a three-plane segmentation approach to improve segmentation accuracy. Method: Our proposed method segments abdominal arteries from an abdominal CT volume with a fully convolutional network (FCN). To segment small arteries, we employ a 2D patch-based segmentation method and an area imbalance reduced training patch generation (AIRTPG) method. AIRTPG adjusts patch number imbalances between patches with artery regions and patches without them. These methods improved the segmentation accuracies of small artery regions. Furthermore, we introduced a three-plane segmentation approach to obtain clear 3D segmentation results from 2D patch-based processes. In the three-plane approach, we performed three segmentation processes using patches generated on axial, coronal, and sagittal planes and combined the results to generate a 3D segmentation result. Results: The evaluation results of the proposed method using 20 cases of abdominal CT volumes show that the averaged F-measure, precision, and recall rates were 87.1%, 85.8%, and 88.4%, respectively. This result outperformed our previous automated FCN-based segmentation method. Our method offers competitive performance compared to the previous blood vessel segmentation methods from 3D volumes. Conclusions: We developed an abdominal artery segmentation method using FCN. The 2D patch-based and AIRTPG methods effectively segmented the artery regions. In addition, the three-plane approach generated good 3D segmentation results.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 Springer
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1007/s11548-019-02062-5
ISSN(print)
収録物識別子タイプ PISSN
収録物識別子 1861-6410
書誌情報 en : International Journal of Computer Assisted Radiology and Surgery

巻 14, 号 12, p. 2069-2081, 発行日 2019-12
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