In this paper from our group, Sunil Yadav and colleagues have developed and validated a 3D fovea morphometry, which clearly outperforms previous approaches in the diseases of interest, mainly autoimmune neuroinflammatory disorders.
Sunil Kumar Yadav, Seyedamirhosein Motamedi, Timm Oberwahrenbrock, Frederike Cosima Oertel, Konrad Polthier, Friedemann Paul, Ella Maria Kadas, and Alexander U. Brandt
Optical coherence tomography (OCT) allows three-dimensional (3D) imaging of the retina, and is commonly used for assessing pathological changes of fovea and macula in many diseases. Many neuroinflammatory conditions are known to cause modifications to the fovea shape. In this paper, we propose a method for parametric modeling of the foveal shape. Our method exploits invariant features of the macula from OCT data and applies a cubic Bézier polynomial along with a least square optimization to produce a best fit parametric model of the fovea. Additionally, we provide several parameters of the foveal shape based on the proposed 3D parametric modeling. Our quantitative and visual results show that the proposed model is not only able to reconstruct important features from the foveal shape, but also produces less error compared to the state-of-the-art methods. Finally, we apply the model in a comparison of healthy control eyes and eyes from patients with neuroinflammatory central nervous system disorders and optic neuritis, and show that several derived model parameters show significant differences between the two groups.