Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images

The 3-D spectral-domain optical coherence tomography (SD-OCT) images of the retina often do not reflect the true shape of the retina and are distorted differently along the x and y axes. In this paper, we propose a novel technique that uses thin-plate splines in two stages to estimate and correct the distinct axial artifacts in SD-OCT images. The method was quantitatively validated using nine pairs of OCT scans obtained with orthogonal fast-scanning axes, where a segmented surface was compared after both datasets had been corrected. The mean unsigned difference computed between the locations of this artifact-corrected surface after the single-spline and dual-spline correction was 23.36 ± 4.04 μm and 5.94 ± 1.09 μm, respectively, and showed a significant difference (p < 0.001 from two-tailed paired t-test). The method was also validated using depth maps constructed from stereo fundus photographs of the optic nerve head, which were compared to the flattened top surface from the OCT datasets. Significant differences (p < 0.001) were noted between the artifact-corrected datasets and the original datasets, where the mean unsigned differences computed over 30 optic-nerve-head-centered scans (in normalized units) were 0.134 ± 0.035 and 0.302 ± 0.134, respectively

Review on retrospective procedures to correct retinal motion artefacts in OCT imaging

Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments

Methods for automated analysis of macular OCT data

Optical coherence tomography (OCT) is fast becoming one of the most important modalities for imaging the eye. It provides high resolution, cross-sectional images of the retina in three dimensions, distinctly showing its many layers. These layers are critical for normal eye function, and vision loss may occur when they are altered by disease. Specifically, the thickness of individual layers can change over time, thereby making the ability to accurately measure these thicknesses an important part of learning about how different diseases affect the eye. Since manual segmentation of the layers in OCT data is time consuming and tedious, automated methods are necessary to extract layer thicknesses. While a standard set of tools exist on the scanners to automatically segment the retina, the output is often limited, providing measurements restricted to only a few layers. Analysis of longitudinal data is also limited, with scans from the same subject often processed independently and registered using only a single landmark at the fovea. Quantification of other changes in the retina, including the accumulation of fluid, are also generally unavailable using the built-in software. In this thesis, we present four contributions for automatically processing OCT data, specifically for data acquired from the macular region of the retina. First, we present a layer segmentation algorithm to robustly segment the eight visible layers of the retina. Our approach combines the use of a random forest (RF) classifier, which produces boundary probabilities, with a boundary refinement algorithm to find surfaces maximizing the RF probabilities. Second, we present a pair of methods for processing longitudinal data from individual subjects: one combining registration and motion correction, and one for simultaneously segmenting the layers across all scans. Third, we develop a method for segmentation of microcystic macular edema, which appear as small, fluid-filled, cystoid spaces within the retina. Our approach again uses an RF classifier to produce a robust segmentation. Finally, we present the development of macular flatspace (MFS), a computational domain used to put data from different subjects in a common coordinate system where each layer appears flat, thereby simplifying any automated processing. We present two applications of MFS: inhomogeneity correction to normalize the intensities within each layer, and layer segmentation by adapting and simplifying a graph formulation used previously

Image analytic tools for tissue characterization using optical coherence tomography

Optical coherence tomography (OCT) has been emerging as a promising imaging technique, with a strong capability of non-invasive, in vivo, high resolution, depth-resolved imaging. There is a great potential to use OCT to guide the treatment of arrhythmias, to prevent preterm birth, and to detect breast cancer. To facilitate the clinical applications, this thesis presents three image analytic tools to characterize biological tissue: 1) automated fiber direction analysis; 2) automated volumetric stitching; 3) automated tissue classification. The fiber direction analysis consists of a particle-filter-based 3D tractography scheme and a pixel-wise fiber analysis scheme. The stitching algorithm enlarges the field of view of current OCT system from millimeter to centimeter level by volumetric stitching using scale-invariant feature transform. Based on relevance vector machine, a region-based classification scheme and a grid-based classification scheme are developed to automatically identify tissue composition in human cardiac tissue and human breast tissue. These tools are collaboratively used to study OCT images from cardiac, cervical, and breast tissue. In cardiac tissue, we apply the fiber orientation analysis to reconstruct 3D cardiac myofibers tractography and perform pixel-wise fiber analysis on the collagen region within human heart. In addition, we apply the region-based algorithm to segment and classify tissue compositions, such as collagen, adipose tissue, fibrotic myocardium, and normal myocardium, over a single or a stitched OCT volume. Using our algorithm, we observe fiber directionality change over depths and find that the fiber orientation changes more dramatically in atria than in ventricle. We also observe different dispersion patterns within collagen layer. In cervical tissue, our stitching algorithm enables a paramount 3D view of entire axial slices. Together with pixel-wise fiber orientation scheme, we analyze the difference of dispersion property within inner/outer regions of four quadrants. We observe two dispersion patterns in pregnant and non-pregnant cervical tissue at the location close to upper cervix. In addition, we discover that an increasing trend of dispersion and an increasing trend of penetration depth from internal orifice (os) to external os. In breast tissue, we visualize various features in both benign and malignant tissues such as invasive ductal carcinoma (IDC), ductal carcinoma in situ, cyst, and terminal duct lobule unit in stitched OCT images. Focusing on the automated detection of IDC, we propose a hierarchy framework of classification model and apply our classifier in two OCT systems and achieve both reasonable sensitivity and specificity in identifying cancerous region

Deformable Image Registration in the Analysis of Multiple Sclerosis

In medical image analysis, image registration is the task of finding corresponding features in two or more images, and using them to solve for the transformation that best aligns the images. Knowing the alignment allows information, such as landmarks and functional metrics, to be easily transferred between images, and allows them to be analyzed together. This dissertation focuses on the development of deformable image registration techniques for the analysis of multiple sclerosis (MS), a neurodegenerative disease that damages the myelin sheath of nervous tissue. MS is known to affect the entire central nervous system (CNS), and can result in the loss of sensorimotor control, cognition, and vision. Hence, the four primary contributions of this dissertation are on the development and application of deformable image registration in the three areas of the CNS that are most currently studied for MS -- the spinal cord, the retina, and the brain. First, for spinal cord magnetic resonance imaging (MRI), an approach is presented that uses deformable registration to provide atlas priors for automatic topology-preserving segmentation of the spinal cord and cerebrospinal fluid. The method shows high accuracy and robustness when compared to manual raters, and allows spinal cord atrophy to be analyzed on large datasets without manual segmentations. Second, for spinal cord diffusion tensor imaging, a pipeline is presented that uses deformable registration to correct for susceptibility distortions in the images. The pipeline allows for accurate computation of spinal cord diffusion metrics, which are shown to be significantly correlated with clinical measures of sensorimotor function and disability levels. Third, for optical coherence tomography (OCT) of the retina, a deformable registration technique is presented that constrains the transformation to follow the OCT acquisition geometry. 3D voxel-based analysis using the algorithm found significant differences between healthy and MS cohorts in regions of the retina that is consistent with previous findings using 2D analysis. Lastly, for brain MRI, a multi-channel registration framework is presented that can use distance transforms and image synthesis to improve registration accuracy. Together, these techniques have enabled several types of analysis that were previously unavailable for the study of MS