Automatic quality evaluation as assessment standard for optical coherence tomography

Josef Kauer; Kay Gawlik; Hanna G. Zimmermann; Ella Maria Kadas; Charlotte Bereuter; Friedemann Paul; Alexander U. Brandt; Frank Haußer; Ingeborg E. Beckers

doi:10.1117/12.2510393

26 February 2019 Automatic quality evaluation as assessment standard for optical coherence tomography

Josef Kauer, Kay Gawlik, Hanna G. Zimmermann, Ella Maria Kadas, Charlotte Bereuter, Friedemann Paul, Alexander U. Brandt, Frank Haußer, Ingeborg E. Beckers

Author Affiliations +

Proceedings Volume 10868, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII; 1086814 (2019) https://doi.org/10.1117/12.2510393
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Retinal optical coherence tomography (OCT) is increasingly used for quantifying neuroaxonal damage in diseases of the central nervous system such as multiple sclerosis. High-quality OCT images are essential for accurate intraretinal segmentation and for correct quantification of retinal thickness changes. The quality of OCT images depends largely on the operator and patient compliance. Quality evaluation is time-consuming, and current OCT image quality criteria depend on the experience of the grader and are therefore subjective. The automatic graderindependent real-time feedback system for quality evaluation of retinal OCT images, AQuA, was developed to standardize quality evaluation and data accuracy. It classifies by signal quality, anatomical completeness and segmentation plausibility and has been validated by experienced graders. However, it is currently limited to OCT scans taken with one device from a single vendor. The aim of this work is to improve the capability of the AQuA quality classifier to generalize to new data, by developing a convolutional neural network (CNN), AQuANet. Moreover, this CNN may serve as a basic quality classifier, that can be adapted to specific problems by transfer learning. AQuANet is trained on A-Scan batches with quality labels automatically obtained with AQuA. Thus, a large set of training data of about 13000 A-Scan batches could be used, leading to an accuracy of 99.53%.

Conference Presentation

Citation Download Citation

Josef Kauer, Kay Gawlik, Hanna G. Zimmermann, Ella Maria Kadas, Charlotte Bereuter, Friedemann Paul, Alexander U. Brandt, Frank Haußer, and Ingeborg E. Beckers "Automatic quality evaluation as assessment standard for optical coherence tomography", Proc. SPIE 10868, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XVII, 1086814 (26 February 2019); https://doi.org/10.1117/12.2510393

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