Deep-stacked auto encoder for liver segmentation

Mubashir Ahmad; Jian Yang; Danni Ai; Syed Furqan Qadri; Yongtian Wang

doi:10.1007/978-981-10-7389-2_24

Deep-stacked auto encoder for liver segmentation

Mubashir Ahmad
, Jian Yang^*
, Danni Ai
, Syed Furqan Qadri
, Yongtian Wang

^*Corresponding author for this work

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

27 Scopus citations

Abstract

Deep learning methods have been successfully applied to feature learning in medical applications. In this paper, we proposed a Deep Stacked Auto-Encoder (DSAE) for liver segmentation from CT images. The proposed method composes of three major steps. First, we learned the features with unlabeled data using the auto encoder. Second, these features are fine-tuned to classify the liver among other abdominal organs. Using this technique we got promising classification results on 2D CT data. This classification of the data helps to segment the liver from the abdomen. Finally, segmentation of a liver is refined by post processing method. We focused on the high accuracy of the classification task because of its effect on the accuracy of a better segmentation. We trained the deep stacked auto encoder (DSAE) on 2D CT images and experimentally shows that this method has high classification accuracy and can speed up the clinical task to segment the liver. The mean DICE coefficient is noted to be 90.1% which is better than the state of art methods.

Original language	English
Title of host publication	Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers
Editors	Xiaoru Yuan, Henry Been-Lirn Duh, Yongtian Wang, Yue Liu, Jian Yang, Shengjin Wang, Ran He
Publisher	Springer Verlag
Pages	243-251
Number of pages	9
ISBN (Print)	9789811073885
DOIs	https://doi.org/10.1007/978-981-10-7389-2_24
State	Published - 2018
Externally published	Yes
Event	12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017 - Beijing, China Duration: 30 Jun 2017 → 1 Jul 2017

Publication series

Name	Communications in Computer and Information Science
Volume	757
ISSN (Print)	1865-0929

Conference

Conference	12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017
Country/Territory	China
City	Beijing
Period	30/06/17 → 1/07/17

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2018.

Keywords

Classification
Deep learning
Liver
Segmentation

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-981-10-7389-2_24

Cite this

Ahmad, M., Yang, J., Ai, D., Qadri, S. F., & Wang, Y. (2018). Deep-stacked auto encoder for liver segmentation. In X. Yuan, H. B.-L. Duh, Y. Wang, Y. Liu, J. Yang, S. Wang, & R. He (Eds.), Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers (pp. 243-251). (Communications in Computer and Information Science; Vol. 757). Springer Verlag. https://doi.org/10.1007/978-981-10-7389-2_24

Ahmad, Mubashir ; Yang, Jian ; Ai, Danni et al. / Deep-stacked auto encoder for liver segmentation. Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers. editor / Xiaoru Yuan ; Henry Been-Lirn Duh ; Yongtian Wang ; Yue Liu ; Jian Yang ; Shengjin Wang ; Ran He. Springer Verlag, 2018. pp. 243-251 (Communications in Computer and Information Science).

@inproceedings{9ea2811c53ce43db9c14bda11b75d81e,

title = "Deep-stacked auto encoder for liver segmentation",

abstract = "Deep learning methods have been successfully applied to feature learning in medical applications. In this paper, we proposed a Deep Stacked Auto-Encoder (DSAE) for liver segmentation from CT images. The proposed method composes of three major steps. First, we learned the features with unlabeled data using the auto encoder. Second, these features are fine-tuned to classify the liver among other abdominal organs. Using this technique we got promising classification results on 2D CT data. This classification of the data helps to segment the liver from the abdomen. Finally, segmentation of a liver is refined by post processing method. We focused on the high accuracy of the classification task because of its effect on the accuracy of a better segmentation. We trained the deep stacked auto encoder (DSAE) on 2D CT images and experimentally shows that this method has high classification accuracy and can speed up the clinical task to segment the liver. The mean DICE coefficient is noted to be 90.1\% which is better than the state of art methods.",

keywords = "Classification, Deep learning, Liver, Segmentation",

author = "Mubashir Ahmad and Jian Yang and Danni Ai and Qadri, \{Syed Furqan\} and Yongtian Wang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017 ; Conference date: 30-06-2017 Through 01-07-2017",

year = "2018",

doi = "10.1007/978-981-10-7389-2\_24",

language = "English",

isbn = "9789811073885",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "243--251",

editor = "Xiaoru Yuan and Duh, \{Henry Been-Lirn\} and Yongtian Wang and Yue Liu and Jian Yang and Shengjin Wang and Ran He",

booktitle = "Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers",

address = "Germany",

}

Ahmad, M, Yang, J, Ai, D, Qadri, SF & Wang, Y 2018, Deep-stacked auto encoder for liver segmentation. in X Yuan, HB-L Duh, Y Wang, Y Liu, J Yang, S Wang & R He (eds), Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers. Communications in Computer and Information Science, vol. 757, Springer Verlag, pp. 243-251, 12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017, Beijing, China, 30/06/17. https://doi.org/10.1007/978-981-10-7389-2_24

Deep-stacked auto encoder for liver segmentation. / Ahmad, Mubashir; Yang, Jian; Ai, Danni et al.
Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers. ed. / Xiaoru Yuan; Henry Been-Lirn Duh; Yongtian Wang; Yue Liu; Jian Yang; Shengjin Wang; Ran He. Springer Verlag, 2018. p. 243-251 (Communications in Computer and Information Science; Vol. 757).

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

TY - GEN

T1 - Deep-stacked auto encoder for liver segmentation

AU - Ahmad, Mubashir

AU - Yang, Jian

AU - Ai, Danni

AU - Qadri, Syed Furqan

AU - Wang, Yongtian

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2018.

PY - 2018

Y1 - 2018

N2 - Deep learning methods have been successfully applied to feature learning in medical applications. In this paper, we proposed a Deep Stacked Auto-Encoder (DSAE) for liver segmentation from CT images. The proposed method composes of three major steps. First, we learned the features with unlabeled data using the auto encoder. Second, these features are fine-tuned to classify the liver among other abdominal organs. Using this technique we got promising classification results on 2D CT data. This classification of the data helps to segment the liver from the abdomen. Finally, segmentation of a liver is refined by post processing method. We focused on the high accuracy of the classification task because of its effect on the accuracy of a better segmentation. We trained the deep stacked auto encoder (DSAE) on 2D CT images and experimentally shows that this method has high classification accuracy and can speed up the clinical task to segment the liver. The mean DICE coefficient is noted to be 90.1% which is better than the state of art methods.

AB - Deep learning methods have been successfully applied to feature learning in medical applications. In this paper, we proposed a Deep Stacked Auto-Encoder (DSAE) for liver segmentation from CT images. The proposed method composes of three major steps. First, we learned the features with unlabeled data using the auto encoder. Second, these features are fine-tuned to classify the liver among other abdominal organs. Using this technique we got promising classification results on 2D CT data. This classification of the data helps to segment the liver from the abdomen. Finally, segmentation of a liver is refined by post processing method. We focused on the high accuracy of the classification task because of its effect on the accuracy of a better segmentation. We trained the deep stacked auto encoder (DSAE) on 2D CT images and experimentally shows that this method has high classification accuracy and can speed up the clinical task to segment the liver. The mean DICE coefficient is noted to be 90.1% which is better than the state of art methods.

KW - Classification

KW - Deep learning

KW - Liver

KW - Segmentation

UR - https://www.scopus.com/pages/publications/85036460446

U2 - 10.1007/978-981-10-7389-2_24

DO - 10.1007/978-981-10-7389-2_24

M3 - Conference contribution

AN - SCOPUS:85036460446

SN - 9789811073885

T3 - Communications in Computer and Information Science

SP - 243

EP - 251

BT - Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers

A2 - Yuan, Xiaoru

A2 - Duh, Henry Been-Lirn

A2 - Wang, Yongtian

A2 - Liu, Yue

A2 - Yang, Jian

A2 - Wang, Shengjin

A2 - He, Ran

PB - Springer Verlag

T2 - 12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017

Y2 - 30 June 2017 through 1 July 2017

ER -

Ahmad M, Yang J, Ai D, Qadri SF, Wang Y. Deep-stacked auto encoder for liver segmentation. In Yuan X, Duh HBL, Wang Y, Liu Y, Yang J, Wang S, He R, editors, Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers. Springer Verlag. 2018. p. 243-251. (Communications in Computer and Information Science). doi: 10.1007/978-981-10-7389-2_24

Deep-stacked auto encoder for liver segmentation

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this