Autonomous Neurosurgical Instrument Segmentation using End-to-End Learning
Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Recommended citation: Kalavakonda, Niveditha, Zeeshan Qazi, Laligam Sekhar, Blake Hannaford. "Neurosurgical and Robotic Instrument Segmentation using Convolutional Neural Networks". Workshop Proceedings in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ; 2019 June 16-20; Long Beach, California.
Abstract:
Monitoring surgical instruments is an essential task in computer-assisted interventions and surgical robotics.
It is also important for navigation, data analysis, skill assessment and surgical workflow analysis in conventional surgery.
However, there are no standard datasets and benchmarks for tool identification in neurosurgery. To this end, we are releasing a novel neurosurgical instrument segmentation dataset called NeuroID for advancing research in the field. Delineating surgical tools from the background requires accurate pixel-wise instrument segmentation. In this paper, we present a comparison between three encoder-decoder approaches to binary segmentation of neurosurgical instruments, where we classify each pixel in the image tobe either tool or background. A baseline performance was obtained by using heuristics to combine extracted features.We also extend the analysis to a publicly available robotic instrument segmentation dataset and include its results. The source code for our methods and the neurosurgical instrument dataset will be made publicly available1to facilitate reproducibility.
Recommended citation: Kalavakonda, Niveditha, Zeeshan Qazi, Laligam Sekhar, Blake Hannaford. “Neurosurgical and Robotic Instrument Segmentation using Convolutional Neural Networks”. Workshop Proceedings in IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2019 June 16-20; Long Beach, California.