bibtext: @InProceedings{ Wit_SimulationBased_MICCAI2024,
                   author = { Wittmann, Bastian and Glandorf, Lukas and Paetzold, Johannes C. and Amiranashvili, Tamaz and Wälchli, Thomas and Razansky, Daniel and Menze, Bjoern },
                   title = { { Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: Segmentation of blood vessels in murine cerebral 3D OCTA images is foundational for in vivo quantitative analysis of the effects of neurovascular disorders, such as stroke or Alzheimer’s, on the vascular network. However, to accurately segment blood vessels with state-of-the-art deep learning methods, a vast amount of voxel-level annotations is required. Since cerebral 3D OCTA images are typically plagued by artifacts and generally have a low signal-to-noise ratio, acquiring manual annotations poses an especially cumbersome and time-consuming task. To alleviate the need for manual annotations, we propose utilizing synthetic data to supervise segmentation algorithms. To this end, we extract patches from vessel graphs and transform them into synthetic cerebral 3D OCTA images paired with their matching ground truth labels by simulating the most dominant 3D OCTA artifacts. In extensive experiments, we demonstrate that our approach achieves competitive results, enabling annotation-free blood vessel segmentation in cerebral 3D OCTA images.
title: Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
authors: Wittmann, Bastian Glandorf, Lukas Paetzold, Johannes C. Amiranashvili, Tamaz Wälchli, Thomas Razansky, Daniel Menze, Bjoern