Three-Dimensional Optical Coherence Tomography (3D-OCT) Image Enhancement with Segmentation-Free Contour Modeling C-Mode

Medical imaging has long played a critical role in diagnosing and assessing various pathologic conditions. In the past decade alone, along with the rapid evolution of computer technologies, various new imaging modalities have been introduced. These provide three-dimensional (3D) image datasets and are proving themselves to be clinically useful. One of the main advantages afforded with a 3D image dataset is flexible image visualization capability. One can slice the target tissue in any arbitrary plane, allowing clinicians to study abnormalities from various vantage points (e.g., sagittal, coronal, and horizontal sections). Optical coherence tomography (OCT) was developed in 1991 and was commercially introduced to ophthalmology in 1996.1,2 First-generation OCT instruments provided a 2D cross-sectional view of the human retina at a microscopic resolution (10 μm) in vivo. Because of its noncontact and noninvasive imaging capabilities, OCT quickly became an indispensable clinical tool for assessing retinal abnormalities, including the leading causes of blindness: age-related macular degeneration, diabetic retinopathy, and glaucoma.2–5 New techniques based on spectral/Fourier domain detection have achieved approximately 50× increases in imaging speeds.6–8 With its fast axial imaging rate, SD-OCT has enabled us to obtain relatively high-density 3D cube data of the human retina within a reasonable scan time (e.g., 27 kHz sampling or 200 × 200 × 1024 samples in approximately 1.48 seconds; Fig. 1a). Although conventional C-mode images along the planes perpendicular to the scanning axis provide an interesting perspective, interpretation of such C-mode images is not intuitive because multiple different retinal layers are sliced in the same view given the natural spherical curvature of the eye (Fig. 1b).9,10 In addition to this natural tissue curvature, an eye moves along the z-axis on a microscopic scale even within the short scanning time, leading to a distorted 3D SOCT image dataset (Fig. 1c). Figure 1 (a) SD-OCT sample images showing (left) horizontal slice and (right) circular slice. (b) Conventional C-mode view of human retina obtained by prototype SD-OCT unit (501 × 180 × 1024 samplings in a 6 × 6 × 1.4-mm region). ... A commercial SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec, Dublin, CA; software version 3.0) was used to solve this problem because it provides the capability of sampling the OCT data set along a segmented layer (internal limiting membrane [ILM], retinal pigment epithelium [RPE], or fitted curve to the RPE) with arbitrary thickness. This segmented C-mode method contains only structures in the segmented layer(s) and provides an improved view compared with conventional C-mode, which intersects multiple tissue planes (Fig. 2). Unfortunately, the quality of the segmented C-mode image depends on the quality of the automated segmentation algorithm performance. The deterioration of segmentation algorithm performance in the presence of retinal pathology is well described (Fig. 3).11,12 Figure 2 OCT segmented C-mode image. C-mode image (left) shows a projection from the ILM (right, within light blue lines) with a thickness of 23 μm. Figure 3 Segmentation error sample. The segmented ILM border (top, light blue line) is not following the ILM properly at the cystic lesion (arrows) because of unusual retinal structure and poor signal quality. The smoothed fitted line (magenta) of the RPE also ... The purpose of this study was to develop a method to easily isolate structures of interest (SoIs) while generating C-mode images in a semiautomated fashion and to describe the clinical applications of such a technique.