Computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas

Shang Wang; Narendran Sudheendran; Chih Hao Liu; Ravi Kiran Manapuram; Valery P. Zakharov; Davis R. Ingram; Alexander J. Lazar; Dina C. Lev; Raphael E. Pollock; Kirill V. Larin

doi:10.1117/12.2006638

Computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas

Shang Wang, Narendran Sudheendran, Chih Hao Liu, Ravi Kiran Manapuram, Valery P. Zakharov, Davis R. Ingram, Alexander J. Lazar, Dina C. Lev, Raphael E. Pollock, Kirill V. Larin

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

2 Scopus citations

Abstract

We present a computational method for the analysis of optical coherence tomography (OCT) images to detect soft tissue sarcomas. The method combines the quantitative analysis of two aspects of information from the intensity A-lines of OCT images; one is the slope of the intensity A-line with dB unit, which is determined by the optical attenuation characteristics of tissue; the other is the standard deviation (SD) of the slope-removed intensity A-line, which is dependent on the tissue structural features. The method is tested with pilot experiments on ex vivo tissue samples of human fat, muscle, well differentiated liposarcoma (WDLS) and leiomyosarcoma. Our results demonstrate the feasibility of this quantitative method in the differentiation of soft tissue sarcomas from normal tissues. This study indicates that OCT can be a potential computer-aided means of automatically and accurately identifying resection margins of soft tissues sarcomas during surgical treatment.

Original language	English (US)
Title of host publication	Dynamics and Fluctuations in Biomedical Photonics IX
DOIs	https://doi.org/10.1117/12.2006638
State	Published - 2013
Event	Dynamics and Fluctuations in Biomedical Photonics VIII Conference - San Francisco, CA, United States Duration: Feb 2 2013 → Feb 4 2013

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	8580
ISSN (Print)	1605-7422

Other

Other	Dynamics and Fluctuations in Biomedical Photonics VIII Conference
Country/Territory	United States
City	San Francisco, CA
Period	2/2/13 → 2/4/13

Keywords

Image processing
Optical coherence tomography
Soft tissue sarcoma

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Biomaterials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging

Access to Document

10.1117/12.2006638

Cite this

Wang, S., Sudheendran, N., Liu, C. H., Manapuram, R. K., Zakharov, V. P., Ingram, D. R., Lazar, A. J., Lev, D. C., Pollock, R. E., & Larin, K. V. (2013). Computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas. In Dynamics and Fluctuations in Biomedical Photonics IX Article 85800T (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8580). https://doi.org/10.1117/12.2006638

Computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas. / Wang, Shang; Sudheendran, Narendran; Liu, Chih Hao et al.
Dynamics and Fluctuations in Biomedical Photonics IX. 2013. 85800T (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8580).

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

Wang, S, Sudheendran, N, Liu, CH, Manapuram, RK, Zakharov, VP, Ingram, DR, Lazar, AJ, Lev, DC, Pollock, RE & Larin, KV 2013, Computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas. in Dynamics and Fluctuations in Biomedical Photonics IX., 85800T, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 8580, Dynamics and Fluctuations in Biomedical Photonics VIII Conference, San Francisco, CA, United States, 2/2/13. https://doi.org/10.1117/12.2006638

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title = "Computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas",

abstract = "We present a computational method for the analysis of optical coherence tomography (OCT) images to detect soft tissue sarcomas. The method combines the quantitative analysis of two aspects of information from the intensity A-lines of OCT images; one is the slope of the intensity A-line with dB unit, which is determined by the optical attenuation characteristics of tissue; the other is the standard deviation (SD) of the slope-removed intensity A-line, which is dependent on the tissue structural features. The method is tested with pilot experiments on ex vivo tissue samples of human fat, muscle, well differentiated liposarcoma (WDLS) and leiomyosarcoma. Our results demonstrate the feasibility of this quantitative method in the differentiation of soft tissue sarcomas from normal tissues. This study indicates that OCT can be a potential computer-aided means of automatically and accurately identifying resection margins of soft tissues sarcomas during surgical treatment.",

keywords = "Image processing, Optical coherence tomography, Soft tissue sarcoma",

author = "Shang Wang and Narendran Sudheendran and Liu, {Chih Hao} and Manapuram, {Ravi Kiran} and Zakharov, {Valery P.} and Ingram, {Davis R.} and Lazar, {Alexander J.} and Lev, {Dina C.} and Pollock, {Raphael E.} and Larin, {Kirill V.}",

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AU - Wang, Shang

AU - Sudheendran, Narendran

AU - Liu, Chih Hao

AU - Manapuram, Ravi Kiran

AU - Zakharov, Valery P.

AU - Ingram, Davis R.

AU - Lazar, Alexander J.

AU - Lev, Dina C.

AU - Pollock, Raphael E.

AU - Larin, Kirill V.

PY - 2013

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N2 - We present a computational method for the analysis of optical coherence tomography (OCT) images to detect soft tissue sarcomas. The method combines the quantitative analysis of two aspects of information from the intensity A-lines of OCT images; one is the slope of the intensity A-line with dB unit, which is determined by the optical attenuation characteristics of tissue; the other is the standard deviation (SD) of the slope-removed intensity A-line, which is dependent on the tissue structural features. The method is tested with pilot experiments on ex vivo tissue samples of human fat, muscle, well differentiated liposarcoma (WDLS) and leiomyosarcoma. Our results demonstrate the feasibility of this quantitative method in the differentiation of soft tissue sarcomas from normal tissues. This study indicates that OCT can be a potential computer-aided means of automatically and accurately identifying resection margins of soft tissues sarcomas during surgical treatment.

AB - We present a computational method for the analysis of optical coherence tomography (OCT) images to detect soft tissue sarcomas. The method combines the quantitative analysis of two aspects of information from the intensity A-lines of OCT images; one is the slope of the intensity A-line with dB unit, which is determined by the optical attenuation characteristics of tissue; the other is the standard deviation (SD) of the slope-removed intensity A-line, which is dependent on the tissue structural features. The method is tested with pilot experiments on ex vivo tissue samples of human fat, muscle, well differentiated liposarcoma (WDLS) and leiomyosarcoma. Our results demonstrate the feasibility of this quantitative method in the differentiation of soft tissue sarcomas from normal tissues. This study indicates that OCT can be a potential computer-aided means of automatically and accurately identifying resection margins of soft tissues sarcomas during surgical treatment.

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KW - Optical coherence tomography

KW - Soft tissue sarcoma

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ER -

Computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas

Abstract

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Keywords

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