Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields

R. Trullo; C. Petitjean; S. Ruan; B. Dubray; D. Nie; D. Shen

doi:10.1109/ISBI.2017.7950685

Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields

R. Trullo
, C. Petitjean
, S. Ruan
, B. Dubray
, D. Nie
, D. Shen

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

77 Citations (Scopus)

Abstract

Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta. 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. Finally, by using Conditional Random Fields (specifically the CRF as Recurrent Neural Network model), we are able to account for relationships between the organs to further improve the segmentation results. Experiments demonstrate competitive performance on a dataset of 30 CT scans.

Original language	English
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Publisher	IEEE Computer Society
Pages	1003-1006
Number of pages	4
ISBN (Electronic)	9781509011711
DOIs	https://doi.org/10.1109/ISBI.2017.7950685
Publication status	Published - 2017 Jun 15
Event	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia Duration: 2017 Apr 18 → 2017 Apr 21

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Other

Other	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	17/4/18 → 17/4/21

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

CRF
CRFasRNN
CT Segmentation
Fully Convolutional Networks (FCN)

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2017.7950685

Cite this

Trullo, R., Petitjean, C., Ruan, S., Dubray, B., Nie, D., & Shen, D. (2017). Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 1003-1006). Article 7950685 (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950685

Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields. / Trullo, R.; Petitjean, C.; Ruan, S. et al.
2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 1003-1006 7950685 (Proceedings - International Symposium on Biomedical Imaging).

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

Trullo, R, Petitjean, C, Ruan, S, Dubray, B, Nie, D & Shen, D 2017, Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950685, Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, pp. 1003-1006, 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, 17/4/18. https://doi.org/10.1109/ISBI.2017.7950685

Trullo R, Petitjean C, Ruan S, Dubray B, Nie D, Shen D. Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society. 2017. p. 1003-1006. 7950685. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2017.7950685

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title = "Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields",

abstract = "Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta. 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. Finally, by using Conditional Random Fields (specifically the CRF as Recurrent Neural Network model), we are able to account for relationships between the organs to further improve the segmentation results. Experiments demonstrate competitive performance on a dataset of 30 CT scans.",

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Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields

Abstract

Publication series

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Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this