Autonomous Neurosurgical Instrument Segmentation Using End-To-End Learning

Niveditha Kalavakonda, Blake Hannaford, Zeeshan Qazi, Laligam Sekhar; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

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 to be 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 available (http://brl.ee.washington.edu/robotics/surgical-robotics/neurosurgical-instrument-segmentation) to facilitate reproducibility.

Related Material


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[bibtex]
@InProceedings{Kalavakonda_2019_CVPR_Workshops,
author = {Kalavakonda, Niveditha and Hannaford, Blake and Qazi, Zeeshan and Sekhar, Laligam},
title = {Autonomous Neurosurgical Instrument Segmentation Using End-To-End Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}