Synthetic data generation for optical flow evaluation in the neurosurgical domain

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.

The Emerging Applications of Synthetic Data in Neurosurgery Research and Practice: A Systematic Review

“…In the domain of surgical planning and execution, the pioneering application of synthetic data in assessing optical flow methods, as delineated by Markus Philipp et al (2021), signifies a substantial advancement. Their endeavors, focused on synthesizing neurosurgical microscope imagery, highlight the indispensable role of synthetic data in algorithmic evaluation and benchmarking.…”