Synthetic data generation for optical flow evaluation in the neurosurgical domain
Markus Philipp, Neal Bacher, Jonas Nienhaus, Lars Hauptmann, Laura Lang, Anna Alperovich, Marielena Gutt‐Will, Andrea Mathis, Stefan C. Saur, Andreas Raabe, Franziska Mathis-Ullrich · Current Directions in Biomedical Engineering · 2021
Abstract Towards computer-assisted neurosurgery, scene understanding algorithms for microscope video data are required. Previous work utilizes optical flow to extract spatiotemporal context from neurosurgical video sequences. However, to select an appropriate optical flow method, we need to analyze which algorithm yields the highest accuracy for the neurosurgical domain. Currently, there are no benchmark datasets available for neurosurgery. In our work, we present an approach to generate synthetic data for optical flow evaluation on the neurosurgical domain. We simulate image sequences and thereby take into account domainspecific visual conditions such as surgical instrument motion. Then, we evaluate two optical flow algorithms, Farneback and PWC-Net, on our synthetic data. Qualitative and quantitative assessments confirm that our data can be used to evaluate optical flow for the neurosurgical domain. Future work will concentrate on extending the method by modeling additional effects in neurosurgery such as elastic background motion.