  At the present time Optical Coherence Tomography (OCT) is among the most
commonly used non-invasive imaging methods for the acquisition of large
volumetric scans of human retinal tissues and vasculature. To resolve decisive
information from extracted OCT volumes and to make it applicable for further
diagnostic analysis, the exact identification of retinal layer thicknesses
serves as an essential task be done for each patient separately. However, the
manual examination of multiple OCT scans in a row is a demanding and time
consuming task, which results in a lengthy qualification process and is
frequently confounded in the presence of tissue-dependent speckle noise.
Therefore, the elaboration of automated segmentation models has become an
important task in the field of medical image processing. We propose a novel,
purely data driven \textit{geometric approach to order-constrained 3D OCT
retinal cell layer segmentation} which takes as input data in any metric space
and comes along with basic operations that can be effectively computed in
parallel. As opposed to many established retina detection methods, our
presented formulation avoids the use of any shape prior and accomplishes the
natural order of the retina in a purely geometric way. This makes the approach
unbiased and hence suited for the detection of local anatomical changes of
retinal tissue structure. To demonstrate robustness of the proposed approach,
we compare two different choices of features on a data set of manually
annotated 3D OCT volumes of healthy human retina. The quality of computed
segmentations is compared to the state of the art in terms of mean absolute
error and the Dice similarity coefficient. The results indicate a great
potential for applying our method to the classification of diseased retina and
opens a new research direction regarding the joint segmentation of retinal cell
layers and blood vessel structures.
