A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina

Raheleh Kafieh, Hossein Rabbani, Saeed Kermani



Optical coherence tomography (OCT) is a powerful imaging modality used to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states, and molecular content [1]. In contrast to OCT technology development which has been a field of active research since 1991, OCT image segmentation has only been more fully explored during the last decade. Segmentation, however, remains one of the most difficult and at the same time most commonly required steps in OCT image analysis. No typical segmentation method exists that can be expected to work equally well for all tasks [2].

One of the most challenging problems in OCT image segmentation is designing a system to work properly in clinical applications. There is no doubt that algorithms and research projects work on a limited number of images with some determinate abnormalities (or even on normal subjects) and such limitations make them more appropriate for bench and not for the bedside. Moreover, OCT images are inherently noisy, thus often requiring the utilization of 3-D contextual information. Furthermore, the structure of the retina can drastically change during disease. Nevertheless, OCT image segmentation is a rapidly growing and important area and a great deal of efforts went into designing algorithms for automatic segmentation of retinal OCTs.

The important steps of OCT image segmentation algorithms may be categorized in to 4 steps: 1) Determining a particular range of OCT datasets (e.g. 2D, 3D, Time Domain, Spectral Domain, macular, ONH, etc.) for which the proposed algorithm will work properly; 2) Allocating proper values for possible parameters of the algorithm; 3) Running the algorithm on determined datasets and acquiring the outcomes (e.g. boundary information, thickness values, classification of normal and abnormal images and etc.); 4) Validating the results by comparing with gold standards or getting the confirmation by an expert.

Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Let’s define 5 separate families of segmentation approaches: Methods applicable to A-scan, B-scan, active contour approaches (frequently in 2-D), analysis methods utilizing artificial intelligence, and segmentation methods using 3D graphs constructed from the 3D OCT volumetric images. Some details of algorithms representing each class will be discussed in more detail and concluding remarks will be provided to compare the efficiency of different methods in different datasets. It should be noted that intrinsic noisy structure of retinal OCTs (particularly in old OCTs and after diseases) makes simple edge detection algorithms unsuitable for this purpose and researchers have tried to develop new algorithms to overcome such problems.

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