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ECAT - Endoscopic Concept Annotation Tool

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MultiMedia Modeling (MMM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11296))

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Abstract

The trend to video documentation in minimally invasive surgery demands for effective and expressive semantic content understanding in order to automatically organize huge and rapidly growing endoscopic video archives. To provide such assistance, deep learning proved to be the means of choice, but requires large amounts of high quality training data labeled by domain experts to produce adequate results. We present a web-based annotation system that provides a very efficient workflow for medical domain experts to conveniently create such video training data with minimum effort.

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Notes

  1. 1.

    http://www.itec.aau.at/ftp/datasets/LapGyn4.

References

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Authors and Affiliations

  1. Institute of Information Technology, Klagenfurt University, Klagenfurt, Austria

    Bernd Münzer, Andreas Leibetseder, Sabrina Kletz & Klaus Schoeffmann

Authors
  1. Bernd Münzer
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  2. Andreas Leibetseder
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  3. Sabrina Kletz
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  4. Klaus Schoeffmann
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Corresponding author

Correspondence to Bernd Münzer .

Editor information

Editors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Ioannis Kompatsiaris

  2. EURECOM, Sophia Antipolis, France

    Benoit Huet

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Vasileios Mezaris

  4. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  5. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  6. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

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Münzer, B., Leibetseder, A., Kletz, S., Schoeffmann, K. (2019). ECAT - Endoscopic Concept Annotation Tool. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11296. Springer, Cham. https://doi.org/10.1007/978-3-030-05716-9_48

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  • DOI: https://doi.org/10.1007/978-3-030-05716-9_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05715-2

  • Online ISBN: 978-3-030-05716-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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