Abstract
Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room. In this work, we tackle the recognition of fine-grained activities, modeled as action triplets \(\langle instrument, verb, target \rangle \) representing the tool activity. To this end, we introduce a new laparoscopic dataset, CholecT40, consisting of 40 videos from the public dataset Cholec80 in which all frames have been annotated using 128 triplet classes. Furthermore, we present an approach to recognize these triplets directly from the video data. It relies on a module called class activation guide, which uses the instrument activation maps to guide the verb and target recognition. To model the recognition of multiple triplets in the same frame, we also propose a trainable 3D interaction space, which captures the associations between the triplet components. Finally, we demonstrate the significance of these contributions via several ablation studies and comparisons to baselines on CholecT40.
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Acknowledgements
This work was supported by French state funds managed within the Investissements dAvenir program by BPI France (project CONDOR) and by the ANR (references ANR-11-LABX-0004 and ANR-16-CE33-0009). The authors would also like to thank the IHU and IRCAD research teams for their help with the data annotation during the CONDOR project.
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Nwoye, C.I. et al. (2020). Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_35
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