Automated detection of esophageal dysplasia in in vivo optical coherence tomography images of the human esophagus

Michalis Kassinopoulos; Jing Dong; Guillermo J. Tearney; Costas Pitris

doi:10.1117/12.2289612

14 February 2018 Automated detection of esophageal dysplasia in in vivo optical coherence tomography images of the human esophagus

Michalis Kassinopoulos, Jing Dong, Guillermo J. Tearney, Costas Pitris

Author Affiliations +

Proceedings Volume 10483, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXII; 104830R (2018) https://doi.org/10.1117/12.2289612
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

Catheter-based Optical Coherence Tomography (OCT) devices allow real-time and comprehensive imaging of the human esophagus. Hence, they provide the potential to overcome some of the limitations of endoscopy and biopsy, allowing earlier diagnosis and better prognosis for esophageal adenocarcinoma patients. However, the large number of images produced during every scan makes manual evaluation of the data exceedingly difficult. In this study, we propose a fully automated tissue characterization algorithm, capable of discriminating normal tissue from Barrett’s Esophagus (BE) and dysplasia through entire three-dimensional (3D) data sets, acquired in vivo. The method is based on both the estimation of the scatterer size of the esophageal epithelial cells, using the bandwidth of the correlation of the derivative (COD) method, as well as intensity-based characteristics. The COD method can effectively estimate the scatterer size of the esophageal epithelium cells in good agreement with the literature. As expected, both the mean scatterer size and its standard deviation increase with increasing severity of disease (i.e. from normal to BE to dysplasia). The differences in the distribution of scatterer size for each tissue type are statistically significant, with a p value of < 0.0001. However, the scatterer size by itself cannot be used to accurately classify the various tissues. With the addition of intensity-based statistics the correct classification rates for all three tissue types range from 83 to 100% depending on the lesion size.

Citation Download Citation

Michalis Kassinopoulos, Jing Dong, Guillermo J. Tearney, and Costas Pitris "Automated detection of esophageal dysplasia in in vivo optical coherence tomography images of the human esophagus", Proc. SPIE 10483, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXII, 104830R (14 February 2018); https://doi.org/10.1117/12.2289612

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Esophagus

Optical coherence tomography

Tissues

In vivo imaging

Image segmentation

Data acquisition

Biopsy

Show All Keywords

Keywords/Phrases

Search In:

Publication Years