Coronary Artery Centerline Tracking With The Morphological Skeleton Loss

Optical Coherence Tomography (OCT) is a widely used non-invasive high resolution 3D imaging technique for biological tissues and plays an important role in ophthalmology. OCT retinal layer segmentation is a fundamental image processing step for OCT-Angiography projection, and disease analysis. A major problem in retinal imaging is the motion artifacts introduced by involuntary eye movements. in this paper, we propose neural networks that jointly correct eye motion and retinal layer segmentation utilizing 3D information, so that the segmentation among neighboring B-scans would be consistent. The experimental results show both visual and quantitative improvements by combining motion correction and segmentation with 3D information compared to conventional and deep learning 2D approaches.