Automated age-related macular degeneration classification in OCT using unsupervised feature learning

Freerk G. Venhuizen; Bram van Ginneken; Bart Bloemen; Mark J. J. P. van Grinsven; Rick Philipsen; Carel Hoyng; Thomas Theelen; Clara I. Sánchez

doi:10.1117/12.2081521

20 March 2015 Automated age-related macular degeneration classification in OCT using unsupervised feature learning

Freerk G. Venhuizen, Bram van Ginneken, Bart Bloemen, Mark J. J. P. van Grinsven, Rick Philipsen, Carel Hoyng, Thomas Theelen, Clara I. Sánchez

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94141I (2015) https://doi.org/10.1117/12.2081521
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Age-related Macular Degeneration (AMD) is a common eye disorder with high prevalence in elderly people. The disease mainly affects the central part of the retina, and could ultimately lead to permanent vision loss. Optical Coherence Tomography (OCT) is becoming the standard imaging modality in diagnosis of AMD and the assessment of its progression. However, the evaluation of the obtained volumetric scan is time consuming, expensive and the signs of early AMD are easy to miss. In this paper we propose a classification method to automatically distinguish AMD patients from healthy subjects with high accuracy. The method is based on an unsupervised feature learning approach, and processes the complete image without the need for an accurate pre-segmentation of the retina. The method can be divided in two steps: an unsupervised clustering stage that extracts a set of small descriptive image patches from the training data, and a supervised training stage that uses these patches to create a patch occurrence histogram for every image on which a random forest classifier is trained. Experiments using 384 volume scans show that the proposed method is capable of identifying AMD patients with high accuracy, obtaining an area under the Receiver Operating Curve of 0:984. Our method allows for a quick and reliable assessment of the presence of AMD pathology in OCT volume scans without the need for accurate layer segmentation algorithms.

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Freerk G. Venhuizen, Bram van Ginneken, Bart Bloemen, Mark J. J. P. van Grinsven, Rick Philipsen, Carel Hoyng, Thomas Theelen, and Clara I. Sánchez "Automated age-related macular degeneration classification in OCT using unsupervised feature learning", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141I (20 March 2015); https://doi.org/10.1117/12.2081521

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