Computerized macular pathology diagnosis in spectral domain optical coherence tomography scans based on multiscale texture and shape features

Spectral-domain optical coherence tomography (SD-OCT) is a noncontact, noninvasive, three-dimensional (3D) imaging technique that performs optical sectioning at micrometer resolution. It is widely used in ophthalmology for identifying the presence of disease and its progression.1 This technology measures the optical back-scattering of the tissues, making it possible to visualize intraocular structures and diagnose ocular diseases, such as glaucoma and macular hole, objectively, and quantitatively. Although OCT imaging technology continues to evolve, technology for automated OCT image analysis and interpretation has not kept pace. With OCT data being generated at increasingly larger amount and higher sampling rates, there is a strong need for automated analysis to support disease diagnosis and tracking. This need is further amplified by the fact that an ophthalmologist making a diagnosis under standard clinical conditions does not have the assistance of a specialist in interpreting OCT data beforehand. A software system that is capable of automated interpretation can potentially assist clinicians in making clinical decisions efficiently in busy daily routines. To our knowledge, there has been no prior work on automated macular pathology identification in OCT images, with the goal of directly predicting the presence probability for each macular pathology in a given cross-sectional frame; this automated method can be helpful to support disease diagnosis especially in situations where qualified readers are not easily accessible. Automated pathology identification in ocular OCT images is complicated by three factors. First, the co-existence of pathologies with other pathologic changes (e.g., epiretinal membrane, vitreous hemorrhage) can complicate the overall appearance, making it challenging to model each pathology individually. Second, there is high appearance variability within each pathology (e.g., in macular hole cases, the holes can have different widths, depths, and shapes, and some can be covered by incompletely detached tissues, making explicit pathology modeling difficult). Third, the measurement of reflectivity of the tissue is affected by the optical properties of the overlying tissues (e.g., opaque media in the vitreous area or blood vessels around retinal surfaces will block or absorb much of the transmitted light respectively, and thus produce shadowing effects). As a result of these factors, attempts to hand craft a set of features or rules to identify each pathology are unlikely to generalize well. Instead, direct encoding of the statistical distribution of low-level image features and training discriminative classifiers based on a large expert-labeled dataset may achieve a more robust performance. In this study, a machine learning–based method for automatically identifying the presence of pathologies from a fovea-centered cross section in a macular SD-OCT scan was developed. Specifically, the presence of the normal macula (NM) and each of the three macular pathologies macular hole (MH), macular edema (ME), and age-related degeneration (AMD) were identified separately in the cross section through the foveal center. This single-frame–based method can serve as a basic component for examining the complete set of frames from the volume. In this work, the automated software that makes diagnostic suggestions is solely based on the interpretation of image appearances, so as to serve as a stand-alone component for OCT image interpretation. Note that for a true clinical diagnosis, all the available information (e.g., the results of OCT image analysis in conjunction with other ancillary tests) would be considered together to make the final diagnostic decision. A preliminary version of this work was presented in our prior paper.2 This article significantly extends our previous publication in several areas: improved automated method, detailed labeling agreement analysis by three ophthalmologists, new ground truth based on majority opinion and complete consensus, evaluation of our original and new method, and several additional experiments, such as effect of training set size, effect of data with inconsistent labeling, and performance on a separate testing dataset collected after the method development stage, which is representative of future unseen data.