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A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation

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

Abstract

Purpose

This paper aims to propose a deep learning-based method for abdominal artery segmentation. Blood vessel structure information is essential to diagnosis and treatment. Accurate blood vessel segmentation is critical to preoperative planning. Although deep learning-based methods perform well on large organs, segmenting small organs such as blood vessels is challenging due to complicated branching structures and positions. We propose a 3D deep learning network from a skeleton context-aware perspective to improve segmentation accuracy. In addition, we propose a novel 3D patch generation method which could strengthen the structural diversity of a training data set.

Method

The proposed method segments abdominal arteries from an abdominal computed tomography (CT) volume using a 3D fully convolutional network (FCN). We add two auxiliary tasks to the network to extract the skeleton context of abdominal arteries. In addition, our skeleton-based patch generation (SBPG) method further enables the FCN to segment small arteries. SBPG generates a 3D patch from a CT volume by leveraging artery skeleton information. These methods improve the segmentation accuracies of small arteries.

Results

We used 20 cases of abdominal CT volumes to evaluate the proposed method. The experimental results showed that our method outperformed previous segmentation accuracies. The averaged precision rate, recall rate, and F-measure were 95.5%, 91.0%, and 93.2%, respectively. Compared to a baseline method, our method improved 1.5% the averaged recall rate and 0.7% the averaged F-measure.

Conclusions

We present a skeleton context-aware 3D FCN to segment abdominal arteries from an abdominal CT volume. In addition, we propose a 3D patch generation method. Our fully automated method segmented most of the abdominal artery regions. The method produced competitive segmentation performance compared to previous methods.

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Acknowledgements

This work were funded by grants from JSPS KAKENHI (26108006, 26560255, 17H00867, 21K19898), the JST CREST (JPMJCR20D5), and the JSPS Bilateral Joint Research Project.

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Authors and Affiliations

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

    Ruiyun Zhu, Masahiro Oda, Yuichiro Hayashi & Kensaku Mori

  2. Information Strategy Office, Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan

    Masahiro Oda & Kensaku Mori

  3. Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan

    Kensaku Mori

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

    Takayuki Kitasaka

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

    Kazunari Misawa

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

    Michitaka Fujiwara

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  1. Ruiyun Zhu
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Correspondence to Ruiyun Zhu or Kensaku Mori.

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Zhu, R., Oda, M., Hayashi, Y. et al. A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation. Int J CARS 18, 461–472 (2023). https://doi.org/10.1007/s11548-022-02767-0

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  • DOI: https://doi.org/10.1007/s11548-022-02767-0

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