Assignment Flow for Order-Constrained OCT Segmentation

TL;DR

This paper introduces a novel geometric, data-driven method for automatically segmenting retinal layers in 3D OCT scans, avoiding shape priors and enabling detection of local tissue changes.

Contribution

It presents a new order-constrained segmentation approach that is unbiased, geometry-based, and applicable in any metric space, differing from existing shape-prior methods.

Findings

High segmentation accuracy compared to state-of-the-art methods

Robustness demonstrated across different feature choices

Potential for disease classification and joint segmentation tasks

Abstract

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.