Fully automated esophagus segmentation with a hierarchical deep learning approach

Roger Trullo; Caroline Petitjean; Dong Nie; Dinggang Shen; Su Ruan

doi:10.1109/ICSIPA.2017.8120664

Fully automated esophagus segmentation with a hierarchical deep learning approach

Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan

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

14 Citations (Scopus)

Abstract

Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	503-506
Number of pages	4
ISBN (Electronic)	9781509055593
DOIs	https://doi.org/10.1109/ICSIPA.2017.8120664
Publication status	Published - 2017
Externally published	Yes
Event	5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 - Kuching, Sarawak, Malaysia Duration: 2017 Sep 12 → 2017 Sep 14

Publication series

Name	Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017

Other

Other	5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017
Country/Territory	Malaysia
City	Kuching, Sarawak
Period	17/9/12 → 17/9/14

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICSIPA.2017.8120664

Cite this

Trullo, R., Petitjean, C., Nie, D., Shen, D., & Ruan, S. (2017). Fully automated esophagus segmentation with a hierarchical deep learning approach. In Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 (pp. 503-506). (Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSIPA.2017.8120664

Fully automated esophagus segmentation with a hierarchical deep learning approach. / Trullo, Roger; Petitjean, Caroline; Nie, Dong; Shen, Dinggang; Ruan, Su.

Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 503-506 (Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017).

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

Trullo, R, Petitjean, C, Nie, D, Shen, D & Ruan, S 2017, Fully automated esophagus segmentation with a hierarchical deep learning approach. in Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017. Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017, Institute of Electrical and Electronics Engineers Inc., pp. 503-506, 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017, Kuching, Sarawak, Malaysia, 17/9/12. https://doi.org/10.1109/ICSIPA.2017.8120664

Trullo R, Petitjean C, Nie D, Shen D, Ruan S. Fully automated esophagus segmentation with a hierarchical deep learning approach. In Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 503-506. (Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017). https://doi.org/10.1109/ICSIPA.2017.8120664

Trullo, Roger ; Petitjean, Caroline ; Nie, Dong ; Shen, Dinggang ; Ruan, Su. / Fully automated esophagus segmentation with a hierarchical deep learning approach. Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 503-506 (Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017).

@inproceedings{8fca2f4d09314462855d7c35a666dd7b,

title = "Fully automated esophagus segmentation with a hierarchical deep learning approach",

abstract = "Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.",

author = "Roger Trullo and Caroline Petitjean and Dong Nie and Dinggang Shen and Su Ruan",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference date: 12-09-2017 Through 14-09-2017",

year = "2017",

doi = "10.1109/ICSIPA.2017.8120664",

language = "English",

series = "Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017",

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

pages = "503--506",

booktitle = "Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017",

}

TY - GEN

T1 - Fully automated esophagus segmentation with a hierarchical deep learning approach

AU - Trullo, Roger

AU - Petitjean, Caroline

AU - Nie, Dong

AU - Shen, Dinggang

AU - Ruan, Su

PY - 2017

Y1 - 2017

N2 - Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

AB - Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

UR - http://www.scopus.com/inward/record.url?scp=85041387177&partnerID=8YFLogxK

U2 - 10.1109/ICSIPA.2017.8120664

DO - 10.1109/ICSIPA.2017.8120664

M3 - Conference contribution

AN - SCOPUS:85041387177

T3 - Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017

SP - 503

EP - 506

BT - Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017

Y2 - 12 September 2017 through 14 September 2017

ER -

Fully automated esophagus segmentation with a hierarchical deep learning approach

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this