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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

Multi-atlas pancreas segmentation: Atlas selection based on vessel structure

http://hdl.handle.net/2237/26966
http://hdl.handle.net/2237/26966
5f471a41-5cc3-411d-bb5e-dc1968d2bfc7
名前 / ファイル ライセンス アクション
PancreasSegmentation.pdf PancreasSegmentation.pdf ファイル公開:2019/07/01 (5.1 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2017-09-06
タイトル
タイトル Multi-atlas pancreas segmentation: Atlas selection based on vessel structure
言語 en
著者 Karasawa, Ken'ichi

× Karasawa, Ken'ichi

WEKO 73488

en Karasawa, Ken'ichi

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Oda, Masahiro

× Oda, Masahiro

WEKO 73489

en Oda, Masahiro

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Kitasaka, Takayuki

× Kitasaka, Takayuki

WEKO 73490

en Kitasaka, Takayuki

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Misawa, Kazunari

× Misawa, Kazunari

WEKO 73491

en Misawa, Kazunari

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Fujiwara, Michitaka

× Fujiwara, Michitaka

WEKO 73492

en Fujiwara, Michitaka

Search repository
Chu, Chengwen

× Chu, Chengwen

WEKO 73493

en Chu, Chengwen

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Zheng, Guoyan

× Zheng, Guoyan

WEKO 73494

en Zheng, Guoyan

Search repository
Rueckert, Daniel

× Rueckert, Daniel

WEKO 73495

en Rueckert, Daniel

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Mori, Kensaku

× Mori, Kensaku

WEKO 73496

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 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
抄録
内容記述 Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%.
言語 en
内容記述タイプ Abstract
出版者
言語 en
出版者 Elsevier
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/10.1016/j.media.2017.03.006
ISSN
収録物識別子タイプ PISSN
収録物識別子 1361-8415
書誌情報 en : Medical Image Analysis

巻 39, p. 18-28, 発行日 2017-07
著者版フラグ
値 author
URI
識別子 https://doi.org/10.1016/j.media.2017.03.006
識別子タイプ DOI
URI
識別子 http://hdl.handle.net/2237/26966
識別子タイプ HDL
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