Automated Segmentation of Retinal Layers from Optical Coherent Tomography Images Using Geodesic Distance
This work addresses the need for automated, accurate segmentation of retinal layers in OCT images to aid in diagnosing and monitoring eye disorders, representing an incremental improvement over prior methods.
The authors tackled the problem of segmenting retinal layers from OCT images by introducing a geodesic distance method (GDM), which outperformed existing algorithms like parametric active contours and graph-based approaches in accuracy for both healthy and pathological cases.
Optical coherence tomography (OCT) is a non-invasive imaging technique that can produce images of the eye at the microscopic level. OCT image segmentation to localise retinal layer boundaries is a fundamental procedure for diagnosing and monitoring the progression of retinal and optical nerve disorders. In this paper, we introduce a novel and accurate geodesic distance method (GDM) for OCT segmentation of both healthy and pathological images in either two- or three-dimensional spaces. The method uses a weighted geodesic distance by an exponential function, taking into account both horizontal and vertical intensity variations. The weighted geodesic distance is efficiently calculated from an Eikonal equation via the fast sweeping method. The segmentation is then realised by solving an ordinary differential equation with the geodesic distance. The results of the GDM are compared with manually segmented retinal layer boundaries/surfaces. Extensive experiments demonstrate that the proposed GDM is robust to complex retinal structures with large curvatures and irregularities and it outperforms the parametric active contour algorithm as well as the graph theoretic based approaches for delineating the retinal layers in both healthy and pathological images.