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Monitoring surgical instruments is an essential task in computer-assisted interventions and surgical robotics. It is also important for navigation, data analysis, skill as- sessment 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 seg- mentation 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 neurosurgi- cal 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 instru- ment dataset will be made publicly available1 to facilitate reproducibility.