Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet

Zhihao Jin; Xue Chen Li; Linlin Shen; Jinyi Lang; Jie Li; Junxiang Wu; Peng Xu; Jiang Duan

doi:10.1109/CBMS49503.2020.00116

Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet

Zhihao Jin
, Xue Chen Li
, Linlin Shen
, Jinyi Lang
, Jie Li
, Junxiang Wu
, Peng Xu
, Jiang Duan

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

8 Citations (Scopus)

Abstract

Nasopharyngeal carcinoma (NPC) is an endemic disease within specific regions in the world. Radiotherapy is the standard treatment for NPC and accurate segmentation of primary gross tumor volume (GTV) is a critical process of continue therapy. In this paper we proposed a ResSE-UNet network and a Ternary Cross-Entropy (TCE) loss function for delineation of GTV. ResSE-UNet employed ResSE blocks to replace convolutional blocks in the original UNet to extract better features, and reduced the number of down-sampling processing to keep relatively high resolution of the images. TCE combined dice loss and Binary cross-entropy loss for larger gradient and better stability in training. The experimental results showed that among all combinations of networks and loss functions, the ResSE-UNet with TCE loss achieved the best segmentation performance, i.e. about 0.84 DSC can be obtained.

Original language	English
Title of host publication	Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020
Editors	Alba Garcia Seco de Herrera, Alejandro Rodriguez Gonzalez, KC Santosh, Zelalem Temesgen, Bridget Kane, Paolo Soda
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	585-590
Number of pages	6
ISBN (Electronic)	9781728194295
DOIs	https://doi.org/10.1109/CBMS49503.2020.00116
Publication status	Published - Jul 2020
Externally published	Yes
Event	33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 - Virtual, Online, United States Duration: 28 Jul 2020 → 30 Jul 2020

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Volume	2020-July
ISSN (Print)	1063-7125

Conference

Conference	33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020
Country/Territory	United States
City	Virtual, Online
Period	28/07/20 → 30/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Free Keywords

GTV segmentation
Nasopharyngeal carcinoma
ResSE-UNet
Ternary cross-entropy

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

Access to Document

10.1109/CBMS49503.2020.00116

Cite this

Jin, Z., Li, X. C., Shen, L., Lang, J., Li, J., Wu, J., Xu, P., & Duan, J. (2020). Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet. In A. G. S. de Herrera, A. Rodriguez Gonzalez, KC. Santosh, Z. Temesgen, B. Kane, & P. Soda (Eds.), Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020 (pp. 585-590). Article 9183362 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS49503.2020.00116

Jin, Zhihao ; Li, Xue Chen ; Shen, Linlin et al. / Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet. Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020. editor / Alba Garcia Seco de Herrera ; Alejandro Rodriguez Gonzalez ; KC Santosh ; Zelalem Temesgen ; Bridget Kane ; Paolo Soda. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 585-590 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

@inproceedings{28dbfa1cc4454ca3b6554f1660913611,

title = "Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet",

abstract = "Nasopharyngeal carcinoma (NPC) is an endemic disease within specific regions in the world. Radiotherapy is the standard treatment for NPC and accurate segmentation of primary gross tumor volume (GTV) is a critical process of continue therapy. In this paper we proposed a ResSE-UNet network and a Ternary Cross-Entropy (TCE) loss function for delineation of GTV. ResSE-UNet employed ResSE blocks to replace convolutional blocks in the original UNet to extract better features, and reduced the number of down-sampling processing to keep relatively high resolution of the images. TCE combined dice loss and Binary cross-entropy loss for larger gradient and better stability in training. The experimental results showed that among all combinations of networks and loss functions, the ResSE-UNet with TCE loss achieved the best segmentation performance, i.e. about 0.84 DSC can be obtained.",

keywords = "GTV segmentation, Nasopharyngeal carcinoma, ResSE-UNet, Ternary cross-entropy",

author = "Zhihao Jin and Li, \{Xue Chen\} and Linlin Shen and Jinyi Lang and Jie Li and Junxiang Wu and Peng Xu and Jiang Duan",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 ; Conference date: 28-07-2020 Through 30-07-2020",

year = "2020",

month = jul,

doi = "10.1109/CBMS49503.2020.00116",

language = "English",

series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

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

pages = "585--590",

editor = "\{de Herrera\}, \{Alba Garcia Seco\} and \{Rodriguez Gonzalez\}, Alejandro and KC Santosh and Zelalem Temesgen and Bridget Kane and Paolo Soda",

booktitle = "Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020",

address = "United States",

}

Jin, Z, Li, XC, Shen, L, Lang, J, Li, J, Wu, J, Xu, P & Duan, J 2020, Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet. in AGS de Herrera, A Rodriguez Gonzalez, KC Santosh, Z Temesgen, B Kane & P Soda (eds), Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020., 9183362, Proceedings - IEEE Symposium on Computer-Based Medical Systems, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., pp. 585-590, 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020, Virtual, Online, United States, 28/07/20. https://doi.org/10.1109/CBMS49503.2020.00116

Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet. / Jin, Zhihao; Li, Xue Chen; Shen, Linlin et al.
Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020. ed. / Alba Garcia Seco de Herrera; Alejandro Rodriguez Gonzalez; KC Santosh; Zelalem Temesgen; Bridget Kane; Paolo Soda. Institute of Electrical and Electronics Engineers Inc., 2020. p. 585-590 9183362 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2020-July).

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

TY - GEN

T1 - Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet

AU - Jin, Zhihao

AU - Li, Xue Chen

AU - Shen, Linlin

AU - Lang, Jinyi

AU - Li, Jie

AU - Wu, Junxiang

AU - Xu, Peng

AU - Duan, Jiang

PY - 2020/7

Y1 - 2020/7

N2 - Nasopharyngeal carcinoma (NPC) is an endemic disease within specific regions in the world. Radiotherapy is the standard treatment for NPC and accurate segmentation of primary gross tumor volume (GTV) is a critical process of continue therapy. In this paper we proposed a ResSE-UNet network and a Ternary Cross-Entropy (TCE) loss function for delineation of GTV. ResSE-UNet employed ResSE blocks to replace convolutional blocks in the original UNet to extract better features, and reduced the number of down-sampling processing to keep relatively high resolution of the images. TCE combined dice loss and Binary cross-entropy loss for larger gradient and better stability in training. The experimental results showed that among all combinations of networks and loss functions, the ResSE-UNet with TCE loss achieved the best segmentation performance, i.e. about 0.84 DSC can be obtained.

AB - Nasopharyngeal carcinoma (NPC) is an endemic disease within specific regions in the world. Radiotherapy is the standard treatment for NPC and accurate segmentation of primary gross tumor volume (GTV) is a critical process of continue therapy. In this paper we proposed a ResSE-UNet network and a Ternary Cross-Entropy (TCE) loss function for delineation of GTV. ResSE-UNet employed ResSE blocks to replace convolutional blocks in the original UNet to extract better features, and reduced the number of down-sampling processing to keep relatively high resolution of the images. TCE combined dice loss and Binary cross-entropy loss for larger gradient and better stability in training. The experimental results showed that among all combinations of networks and loss functions, the ResSE-UNet with TCE loss achieved the best segmentation performance, i.e. about 0.84 DSC can be obtained.

KW - GTV segmentation

KW - Nasopharyngeal carcinoma

KW - ResSE-UNet

KW - Ternary cross-entropy

UR - https://www.scopus.com/pages/publications/85091186094

U2 - 10.1109/CBMS49503.2020.00116

DO - 10.1109/CBMS49503.2020.00116

M3 - Conference contribution

AN - SCOPUS:85091186094

T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems

SP - 585

EP - 590

BT - Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020

A2 - de Herrera, Alba Garcia Seco

A2 - Rodriguez Gonzalez, Alejandro

A2 - Santosh, KC

A2 - Temesgen, Zelalem

A2 - Kane, Bridget

A2 - Soda, Paolo

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020

Y2 - 28 July 2020 through 30 July 2020

ER -

Jin Z, Li XC, Shen L, Lang J, Li J, Wu J et al. Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet. In de Herrera AGS, Rodriguez Gonzalez A, Santosh KC, Temesgen Z, Kane B, Soda P, editors, Proceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 585-590. 9183362. (Proceedings - IEEE Symposium on Computer-Based Medical Systems). doi: 10.1109/CBMS49503.2020.00116

Automatic primary gross tumor volume segmentation for nasopharyngeal carcinoma using ResSE-UNet

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this