An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images. (February 2017)

• Record Type: Journal Article
• Authors: Sidibé, Désiré; Sankar, Shrinivasan; Lemaître, Guillaume; Rastgoo, Mojdeh; Massich, Joan; Cheung, Carol Y.; Tan, Gavin S.W.; Milea, Dan; Lamoureux, Ecosse; Wong, Tien Y.; Mériaudeau, Fabrice
• Is Part Of: Computer methods and programs in biomedicine. Volume 139(2017)
• Page Start: 109
• Page End: 117
• Publication Date: 2017-02
• Subjects: Diabetic retinopathy -- Diabetic macular edema -- SD-OCT -- Classification -- Anomaly detection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.11.001
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose a method to detect abnormal B-scans in SD-OCT volumes. We develop an anomaly detection approach for classification of SD-OCT volumes. The proposed method achieves high sensitivity and specificity compared to other supervised classification methods. Abstract: This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works.