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Workshop on Clinical Image-Based Procedures

CLIP 2012: Clinical Image-Based Procedures. From Planning to Intervention pp 67–74Cite as

Automated Segmentation and Anatomical Labeling of Abdominal Arteries Based on Multi-organ Segmentation from Contrast-Enhanced CT Data

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 7761)

Abstract

A fully automated method is described for segmentation and anatomical labeling of the abdominal arteries from contrast-enhanced CT data of the upper abdomen. By assuming that the regions of the organs and aorta have already been automatically segmented, the problem is formulated as extracting and selecting the optimal paths between the organ and aorta regions based on a basic anatomical constraint that arteries supplying blood to an organ consist of tree structures whose root nodes are located in the aorta region and leaf nodes in the organ region. Using the constraint, the proposed method solves both of artery segmentation and anatomical labeling. In addition, the method is robust against topological variability of the branching patterns. Experimental results using 10 datasets demonstrate that the proposed method was effectively applied to several kinds of the abdominal arteries, which include the hepatic, splenic, and renal arteries. The average F-measure, which is a normalized accuracy measure taking both false positives and true negatives into account, was 0.89 for the proposed and 0.74 for the previous methods. The method could also effectively deal with topological variability of the hepatic and renal arteries.

Keywords

  • Renal Artery
  • Hepatic Artery
  • Leaf Node
  • Candidate Region
  • Celiac Artery

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-642-38079-2_9
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Author information

Authors and Affiliations

  1. Graduate School of Information Science and Technology, Japan

    Yuki Suzuki & Yoshinobu Sato

  2. Graduate School of Medicine, Osaka University, Japan

    Yuki Suzuki, Toshiyuki Okada, Masatoshi Hori, Noriyuki Tomiyama & Yoshinobu Sato

  3. Graduate School of Engineering, Kobe University, Japan

    Futoshi Yokota

  4. Children’s National Medical Center, Sheikh Zayed Institute for Pediatric Surgical Innovation, Washington DC, USA

    Marius George Linguraru

Authors
  1. Yuki Suzuki
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  2. Toshiyuki Okada
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  3. Masatoshi Hori
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  4. Futoshi Yokota
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  5. Marius George Linguraru
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  6. Noriyuki Tomiyama
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  7. Yoshinobu Sato
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Editor information

Editors and Affiliations

  1. Cognitive Computing and Medical Imaging, Fraunhofer IGD, Fraunhoferstr. 5, 64283, Darmstadt, Germany

    Klaus Drechsler, Cristina Oyarzun Laura & Stefan Wesarg,  & 

  2. Fraunhofer IDM@NTU, Nanyang Avenue, 639798, Singapore, Singapore

    Marius Erdt

  3. Children’s National Medical Center, Sheikh Zayed Institute for Pediatric Surgical Innovation, 111 Michigan Avenue,NW, 20010, Washington, D.C., USA

    Marius George Linguraru

  4. Interventional Radiology, Georgetown University Hospital, 3800 Reservoir Rd., NW, 20007-2113, Washington, D.C., USA

    Karun Sharma

  5. Children’s National Medical Center, Sheikh Zayed Institute for Pediatric Surgical Innovation, 111Michigan Avenue, NW, 20010, Washington, D.C., USA

    Raj Shekhar

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Suzuki, Y. et al. (2013). Automated Segmentation and Anatomical Labeling of Abdominal Arteries Based on Multi-organ Segmentation from Contrast-Enhanced CT Data. In: , et al. Clinical Image-Based Procedures. From Planning to Intervention. CLIP 2012. Lecture Notes in Computer Science, vol 7761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38079-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-38079-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38078-5

  • Online ISBN: 978-3-642-38079-2

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