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Shahrian Varnousfaderani E, Wu J, Vogl WD, et al. A novel benchmark model for intelligent annotation of spectral-domain optical coherence tomography scans using the example of cyst annotation. Comput Methods Programs Biomed. 2016;130:93-105doi: 10.1016/j.cmpb.2016.03.012.
Shahrian Varnousfaderani, E., Wu, J., Vogl, W. D., Philip, A. M., Montuoro, A., Leitner, R., Simader, C., Waldstein, S. M., Gerendas, B. S., & Schmidt-Erfurth, U. (2016). A novel benchmark model for intelligent annotation of spectral-domain optical coherence tomography scans using the example of cyst annotation. Computer methods and programs in biomedicine, 13093-105. https://doi.org/10.1016/j.cmpb.2016.03.012
Shahrian Varnousfaderani, Ehsan, et al. "A novel benchmark model for intelligent annotation of spectral-domain optical coherence tomography scans using the example of cyst annotation." Computer methods and programs in biomedicine vol. 130 (2016): 93-105. doi: https://doi.org/10.1016/j.cmpb.2016.03.012
Shahrian Varnousfaderani E, Wu J, Vogl WD, Philip AM, Montuoro A, Leitner R, Simader C, Waldstein SM, Gerendas BS, Schmidt-Erfurth U. A novel benchmark model for intelligent annotation of spectral-domain optical coherence tomography scans using the example of cyst annotation. Comput Methods Programs Biomed. 2016 Jul;130:93-105. doi: 10.1016/j.cmpb.2016.03.012. Epub 2016 Mar 16. PMID: 27208525.
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Comput Methods Programs Biomed. 2016 Jul;130:93-105. doi: 10.1016/j.cmpb.2016.03.012. Epub 2016 Mar 16.

A novel benchmark model for intelligent annotation of spectral-domain optical coherence tomography scans using the example of cyst annotation.

Computer methods and programs in biomedicine

Ehsan Shahrian Varnousfaderani, Jing Wu, Wolf-Dieter Vogl, Ana-Maria Philip, Alessio Montuoro, Roland Leitner, Christian Simader, Sebastian M Waldstein, Bianca S Gerendas, Ursula Schmidt-Erfurth

Affiliations

  1. Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria.
  2. Christian Doppler Laboratory for Ophthalmic Image Analysis, Vienna Reading Center, Department of Ophthalmology, Medical University of Vienna, Vienna, Austria. Electronic address: [email protected].

PMID: 27208525 DOI: 10.1016/j.cmpb.2016.03.012

Abstract

BACKGROUND AND OBJECTIVES: The lack of benchmark data in computational ophthalmology contributes to the challenging task of applying disease assessment and evaluate performance of machine learning based methods on retinal spectral domain optical coherence tomography (SD-OCT) scans. Presented here is a general framework for constructing a benchmark dataset for retinal image processing tasks such as cyst, vessel, and subretinal fluid segmentation and as a result, a benchmark dataset for cyst segmentation has been developed.

METHOD: First, a dataset captured by different SD-OCT vendors with different numbers of scans and pathology qualities are selected. Then a robust and intelligent method is used to evaluate performance of readers, partitioning the dataset into subsets. Subsets are then assigned to complementary readers for annotation with respect to a novel confidence based annotation protocol. Finally, reader annotations are combined based on their performance to generate final annotations.

RESULT: The generated benchmark dataset for cyst segmentation comprises 26 SD-OCT scans with differing cyst qualities, collected from 4 different SD-OCT vendors to cover a wide variety of data. The dataset is partitioned into three subsets which are annotated by complementary readers based on a confidence based annotation protocol. Experimental results show annotations of complementary readers are combined efficiently with respect to their performance, generating accurate annotations.

CONCLUSION: Our results facilitate the process of generating benchmark datasets. Moreover the generated benchmark data set for cyst segmentation can be used reliably to train and test machine learning based methods.

Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Keywords: Benchmark dataset; Cyst segmentation; SD-OCT

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