Real-time instrument tracking is a crucial requirement for various computer-assisted interventions. To overcome problems such as specular reflection and motion blur, we propose a novel method that takes advantage of the interdependency between localization and segmentation of the surgical tool. In particular, we reformulate the 2D pose estimation as a heatmap regression and thereby enable a robust, concurrent regression of both tasks via deep learning. Throughout experimental results, we demonstrate that this modeling leads to a significantly better performance than directly regressing the tool position and that our method outperforms the state-of-the-art on a Retinal Microsurgery benchmark and the MICCAI EndoVis Challenge 2015.
CITATION STYLE
Laina, I., Rieke, N., Rupprecht, C., Vizcaíno, J. P., Eslami, A., Tombari, F., & Navab, N. (2017). Concurrent segmentation and localization for tracking of surgical instruments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10434 LNCS, pp. 664–672). Springer Verlag. https://doi.org/10.1007/978-3-319-66185-8_75
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