Anatomical data augmentation for CNN based pixel-wise classification

Avi Ben-Cohen; Eyal Klang; Michal Marianne Amitai; Jacob Goldberger; Hayit Greenspan

doi:10.1109/ISBI.2018.8363762

Anatomical data augmentation for CNN based pixel-wise classification

Avi Ben-Cohen
, Eyal Klang
, Michal Marianne Amitai
, Jacob Goldberger
, Hayit Greenspan

Bar-Ilan University

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

14 Scopus citations

Abstract

In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3% in the success rate, 5% in the classification accuracy, and 4% in Dice.

Original language	English
Title of host publication	2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
Publisher	IEEE Computer Society
Pages	1096-1099
Number of pages	4
ISBN (Electronic)	9781538636367
DOIs	https://doi.org/10.1109/ISBI.2018.8363762
State	Published - 23 May 2018
Event	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States Duration: 4 Apr 2018 → 7 Apr 2018

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2018-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/Territory	United States
City	Washington
Period	4/04/18 → 7/04/18

Funding

Funders	Funder number
Israel Science Foundation	1918/16

Keywords

Augmentation
CT
Liver
Semi-supervised learning

Access to Document

10.1109/ISBI.2018.8363762

Cite this

Ben-Cohen, A., Klang, E., Amitai, M. M., Goldberger, J., & Greenspan, H. (2018). Anatomical data augmentation for CNN based pixel-wise classification. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 1096-1099). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363762

@inproceedings{f31102de80374f19919709fd8913216d,

title = "Anatomical data augmentation for CNN based pixel-wise classification",

abstract = "In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3\% in the success rate, 5\% in the classification accuracy, and 4\% in Dice.",

keywords = "Augmentation, CT, Liver, Semi-supervised learning",

author = "Avi Ben-Cohen and Eyal Klang and Amitai, \{Michal Marianne\} and Jacob Goldberger and Hayit Greenspan",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",

year = "2018",

month = may,

day = "23",

doi = "10.1109/ISBI.2018.8363762",

language = "אנגלית",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "1096--1099",

booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",

address = "ארצות הברית",

}

Ben-Cohen, A, Klang, E , Amitai, MM, Goldberger, J & Greenspan, H 2018, Anatomical data augmentation for CNN based pixel-wise classification. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, IEEE Computer Society, pp. 1096-1099, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/04/18. https://doi.org/10.1109/ISBI.2018.8363762

Anatomical data augmentation for CNN based pixel-wise classification. / Ben-Cohen, Avi; Klang, Eyal ; Amitai, Michal Marianne et al.
2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. p. 1096-1099 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April).

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

TY - GEN

T1 - Anatomical data augmentation for CNN based pixel-wise classification

AU - Ben-Cohen, Avi

AU - Klang, Eyal

AU - Amitai, Michal Marianne

AU - Goldberger, Jacob

AU - Greenspan, Hayit

PY - 2018/5/23

Y1 - 2018/5/23

N2 - In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3% in the success rate, 5% in the classification accuracy, and 4% in Dice.

AB - In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3% in the success rate, 5% in the classification accuracy, and 4% in Dice.

KW - Augmentation

KW - CT

KW - Liver

KW - Semi-supervised learning

UR - https://www.scopus.com/pages/publications/85048123178

U2 - 10.1109/ISBI.2018.8363762

DO - 10.1109/ISBI.2018.8363762

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85048123178

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 1096

EP - 1099

BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018

PB - IEEE Computer Society

T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

Y2 - 4 April 2018 through 7 April 2018

ER -

Anatomical data augmentation for CNN based pixel-wise classification

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this