Assignment Flow for Order-Constrained OCT Segmentation

Abstract At the present time optical coherence tomography (OCT) is among most commonly used non-invasive imaging methods for acquisition of large volumetric scans human retinal tissues and vasculature. The substantial increase accessible highly resolved 3D samples at optic nerve head macula directly linked to medical advancements in early detection eye diseases. To resolve decisive information from extracted OCT volumes make it applicable further diagnostic analysis, exact measurement layer thicknesses serves as an essential task be done each patient separately. However, manual examination a demanding consuming task, which typically made difficult by presence tissue-dependent speckle noise. Therefore, elaboration automated segmentation models has become important field image processing. We propose novel, purely data driven geometric approach order-constrained 3 D cell takes input any metric space can implemented using only simple, parallelizable operations. As opposed many established methods, we use locally features do not employ global shape prior. physiological order layers membranes achieved through introduction smoothed energy term. This combined with additional regularization local smoothness yield accurate segmentations. thereby systematically avoid bias pertaining hence suited anatomical changes tissue structure. demonstrate its robustness, compare two different choices on set manually annotated healthy retina. quality computed segmentations compared state art automatic segmention well ground truth terms mean absolute error Dice similarity coefficient. Visualizations segmented are also provided.