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Automatic 3D liver location and segmentation via convolutional neural network and graph cut

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

Abstract

Purpose

Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans.

Methods

The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map.

Results

The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively.

Conclusions

The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.

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Notes

  1. In detail, they are the livers 02, 04, 06, 08, 10, 12, 14, 16, 18 and 20.

  2. In detail, they are the livers 01, 03, 05, 07, 09, 11, 13, 15, 17 and 19.

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Acknowledgments

The authors would like to thank Dr. Jing Yuan and Dr. Jialin Peng for their valuable discussions and useful suggestions. This work was supported in part by National Natural Science Foundation of China (Grant Nos. 11271323, 91330105, 11401231) and the Zhejiang Provincial Natural Science Foundation of China (Grant No.: LZ13A010002).

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    Authors and Affiliations

    1. School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China

      Fang Lu, Fa Wu, Peijun Hu & Dexing Kong

    2. Department of Radiology, First Affiliated Hospital of Zhejiang University, Hangzhou, 310003, China

      Zhiyi Peng

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    1. Fang Lu
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    Correspondence to Dexing Kong.

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    This article does not contain any studies with human participants or animals performed by any of the authors.

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    Informed consent was obtained from all individual participants included in the study.

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    Fang Lu and Fa Wu have contributed equally to this work.

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    Lu, F., Wu, F., Hu, P. et al. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J CARS 12, 171–182 (2017). https://doi.org/10.1007/s11548-016-1467-3

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