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Four-dimensional fully convolutional residual network-based liver segmentation in Gd-EOB-DTPA-enhanced MRI

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

Abstract

Purpose

Gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) tends to show higher diagnostic accuracy than other modalities. There is a demand for computer-assisted detection (CAD) software for Gd-EOB-DTPA-enhanced MRI. Segmentation with high accuracy is important for CAD software. We propose a liver segmentation method for Gd-EOB-DTPA-enhanced MRI that is based on a four-dimensional (4D) fully convolutional residual network (FC-ResNet). The aims of this study are to determine the best combination of an input image and output image in our proposed method and to compare our proposed method with the previous rule-based segmentation method.

Methods

We prepared a five-phase image set and a hepatobiliary phase image set as the input image sets to determine the best input image set. We also prepared a labeled liver image and labeled liver and labeled body trunk images as the output image sets to determine the best output image set. In addition, we optimized the hyperparameters of our proposed model. We used 30 cases to train our model, 10 cases to determine the hyperparameters of our model, and 20 cases to evaluate our model.

Results

Our network with the five-phase image set and the output image set of labeled liver and labeled body trunk images showed the highest accuracy. Our proposed method showed higher accuracy than the previous rule-based segmentation method. The Dice coefficient of the liver region was 0.944 ± 0.018.

Conclusion

Our proposed 4D FC-ResNet showed satisfactory performance for liver segmentation as preprocessing in CAD software.

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Authors and Affiliations

  1. Department of Computational Diagnostic Radiology and Preventive Medicine, the University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Tomomi Takenaga, Yukihiro Nomura, Masaki Murata, Soichiro Miki, Takeharu Yoshikawa & Naoto Hayashi

  2. Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, 4669-2, Ami, Ami-machi, Inashiki-gun, Ibaraki, Japan

    Tomomi Takenaga

  3. Department of Radiology, the University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Shouhei Hanaoka & Osamu Abe

  4. Faculty of Biology-Oriented Science and Technology, Kindai University, Nishimitani 930, Kinokawa, Wakayama, 649-6493, Japan

    Mitsutaka Nemoto

  5. Radiology and Biomedical Engineering, Graduate School of Medicine, the University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Takahiro Nakao & Osamu Abe

Authors
  1. Tomomi Takenaga
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  2. Shouhei Hanaoka
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  3. Yukihiro Nomura
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  4. Mitsutaka Nemoto
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  5. Masaki Murata
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  6. Takahiro Nakao
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  7. Soichiro Miki
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  8. Takeharu Yoshikawa
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  9. Naoto Hayashi
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  10. Osamu Abe
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Corresponding author

Correspondence to Tomomi Takenaga.

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Conflict of interest

The authors declare no conflicts of interest with regard to the present study.

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Takenaga, T., Hanaoka, S., Nomura, Y. et al. Four-dimensional fully convolutional residual network-based liver segmentation in Gd-EOB-DTPA-enhanced MRI. Int J CARS 14, 1259–1266 (2019). https://doi.org/10.1007/s11548-019-01935-z

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  • DOI: https://doi.org/10.1007/s11548-019-01935-z

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