Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks

Chi Wang; Hong Song; Lei Chen; Qiang Li; Jian Yang; Xiaohua Tony Hu; Le Zhang

doi:10.1109/BIBM.2018.8621257

Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks

Chi Wang, Hong Song^*, Lei Chen, Qiang Li, Jian Yang, Xiaohua Tony Hu, Le Zhang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

Abstract

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of surgical planning, postoperative assessment and hepatic diseases. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of the liver. In this paper, we propose a multi-plane integrated fully convolutional neural network to segment the liver from 3D CT volumes. Our network uses multiple layers of dilated convolution filters to replace traditional ones. Residual connections and multi-scale predictions are also employed in the network to improve the segmentation performance. We extensively evaluated our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method outperformed other state-of-the-art methods with an average Dice score of 96.7% on the segmentation results of liver, which only used a single framework without any pre-processing operation on it.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	518-523
Number of pages	6
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621257
Publication status	Published - 21 Jan 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	3/12/18 → 6/12/18

Keywords

CT
convolutional neural networks
deep learning
liver segmentation
multi-plane integration

Access to Document

10.1109/BIBM.2018.8621257

Cite this

Wang, C., Song, H., Chen, L., Li, Q., Yang, J., Hu, X. T., & Zhang, L. (2019). Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 518-523). Article 8621257 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621257

Wang, Chi ; Song, Hong ; Chen, Lei et al. / Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 518-523 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

@inproceedings{3e7da521b80d4d01a085e1bbd8af7143,

title = "Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks",

abstract = "Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of surgical planning, postoperative assessment and hepatic diseases. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of the liver. In this paper, we propose a multi-plane integrated fully convolutional neural network to segment the liver from 3D CT volumes. Our network uses multiple layers of dilated convolution filters to replace traditional ones. Residual connections and multi-scale predictions are also employed in the network to improve the segmentation performance. We extensively evaluated our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method outperformed other state-of-the-art methods with an average Dice score of 96.7% on the segmentation results of liver, which only used a single framework without any pre-processing operation on it.",

keywords = "CT, convolutional neural networks, deep learning, liver segmentation, multi-plane integration",

author = "Chi Wang and Hong Song and Lei Chen and Qiang Li and Jian Yang and Hu, {Xiaohua Tony} and Le Zhang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

month = jan,

day = "21",

doi = "10.1109/BIBM.2018.8621257",

language = "English",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "518--523",

editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

address = "United States",

}

Wang, C, Song, H, Chen, L, Li, Q, Yang, J, Hu, XT & Zhang, L 2019, Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621257, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 518-523, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621257

Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks. / Wang, Chi; Song, Hong; Chen, Lei et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 518-523 8621257 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks

AU - Wang, Chi

AU - Song, Hong

AU - Chen, Lei

AU - Li, Qiang

AU - Yang, Jian

AU - Hu, Xiaohua Tony

AU - Zhang, Le

PY - 2019/1/21

Y1 - 2019/1/21

N2 - Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of surgical planning, postoperative assessment and hepatic diseases. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of the liver. In this paper, we propose a multi-plane integrated fully convolutional neural network to segment the liver from 3D CT volumes. Our network uses multiple layers of dilated convolution filters to replace traditional ones. Residual connections and multi-scale predictions are also employed in the network to improve the segmentation performance. We extensively evaluated our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method outperformed other state-of-the-art methods with an average Dice score of 96.7% on the segmentation results of liver, which only used a single framework without any pre-processing operation on it.

AB - Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of surgical planning, postoperative assessment and hepatic diseases. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of the liver. In this paper, we propose a multi-plane integrated fully convolutional neural network to segment the liver from 3D CT volumes. Our network uses multiple layers of dilated convolution filters to replace traditional ones. Residual connections and multi-scale predictions are also employed in the network to improve the segmentation performance. We extensively evaluated our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method outperformed other state-of-the-art methods with an average Dice score of 96.7% on the segmentation results of liver, which only used a single framework without any pre-processing operation on it.

KW - CT

KW - convolutional neural networks

KW - deep learning

KW - liver segmentation

KW - multi-plane integration

UR - http://www.scopus.com/inward/record.url?scp=85062496870&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2018.8621257

DO - 10.1109/BIBM.2018.8621257

M3 - Conference contribution

AN - SCOPUS:85062496870

T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 518

EP - 523

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Wang C, Song H, Chen L, Li Q, Yang J, Hu XT et al. Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 518-523. 8621257. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621257

Automatic Liver Segmentation Using Multi-plane Integrated Fully Convolutional Neural Networks

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