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Multi-atlas pancreas segmentation: Atlas selection based on vessel structure
http://hdl.handle.net/2237/26966
http://hdl.handle.net/2237/269665f471a41-5cc3-411d-bb5e-dc1968d2bfc7
名前 / ファイル | ライセンス | アクション |
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PancreasSegmentation.pdf ファイル公開:2019/07/01 (5.1 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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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× Oda, Masahiro× Kitasaka, Takayuki× Misawa, Kazunari× Fujiwara, Michitaka× Chu, Chengwen× Zheng, Guoyan× Rueckert, Daniel× Mori, Kensaku |
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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 |
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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 |