Analysis of macular OCT images using deformable registration

Abstract

Optical coherence tomography (OCT) of the macula has become increasingly important in the investigation of retinal pathology. However, deformable image registration, which is used for aligning subjects for pairwise comparisons, population averaging, and atlas label transfer, has not been well-developed and demonstrated on OCT images. In this paper, we present a deformable image registration approach designed specifically for macular OCT images. The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries. Finally, the layers within the retina are aligned by a deformable registration using one-dimensional radial basis functions. The algorithm was validated using manual delineations of retinal layers in OCT images from a cohort consisting of healthy controls and patients diagnosed with multiple sclerosis (MS). We show that the algorithm overcomes the shortcomings of existing generic registration methods, which cannot be readily applied to OCT images. A successful deformable image registration algorithm for macular OCT opens up a variety of population based analysis techniques that are regularly used in other imaging modalities, such as spatial normalization, statistical atlas creation, and voxel based morphometry. Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease. In particular, included is a pilot study of localized volumetric changes between healthy controls and MS patients using the proposed registration algorithm.

Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework

“…In Chen et al (Chen et al, 2014), registration of macular OCT scans used rigid alignment of foveae, affine registration of A-scans to match the inner and outer retinal boundaries, and smooth deformation of A-scans using radial basis functions for refined alignment of the retinal layers. Lee et al (Lee et al, 2015) extended upon (Gibson et al, 2010) and represented segmented retinal surfaces utilizing the framework of mathematical currents.…”

Retinal layer parcellation of optical coherence tomography images: Data resource for multiple sclerosis and healthy controls

“…Automatic segmentation methods evaluated using this dataset can be found in Refs. [1] , [2] , [3] , [4] , [5] , [6] and other works using this data, in whole or part, include [7] , [8] , [9] , [10] , [11] , [12] .…”

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

“…With the estimated deformation field, atlas labels were transformed to each target space and the Dice similarity coefficient between each of the eight layers was reported. Chen et al [77] (2014) followed the previous conference publication and extended the work in a journal paper. A larger validation cohort consisting of OCT images of the right eyes from 45 subjects (19 healthy controls and 26 patients diagnosed with multiple sclerosis) was used.…”