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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

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.

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Acknowledgements

Parts of this research were supported by MEXT, JSPS KAKENHI Grant Numbers 26108006, 17H00867, JSPS Bilateral International Collaboration Grants, and JST ACT-I (JPMJPR16U9).

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Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan

    Masahiro Oda, Holger R. Roth & Kensaku Mori

  2. School of Information Science, Aichi Institute of Technology, 1247 Yachigusa, Yakusa-cho, Toyota, Aichi, Japan

    Takayuki Kitasaka

  3. Aichi Cancer Center Hospital, 1-1 Kanokoden, Chikusa-ku, Nagoya, Aichi, Japan

    Kazunari Misawa

  4. Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan

    Michitaka Fujiwara

  5. Research Center for Medical Bigdata, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan

    Kensaku Mori

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  1. Masahiro Oda
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Correspondence to Masahiro Oda.

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Oda, M., Roth, H.R., Kitasaka, T. et al. Abdominal artery segmentation method from CT volumes using fully convolutional neural network. Int J CARS 14, 2069–2081 (2019). https://doi.org/10.1007/s11548-019-02062-5

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  • DOI: https://doi.org/10.1007/s11548-019-02062-5

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