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Publication Detail
Large scale simulation of labeled intraoperative scenes in unity
  • Publication Type:
    Journal article
  • Authors:
    Dowrick T, Davidson B, Gurusamy K, Clarkson MJ
  • Publisher:
    Springer Science and Business Media LLC
  • Publication date:
  • Journal:
    International Journal of Computer Assisted Radiology and Surgery
  • Medium:
  • Status:
  • Country:
  • PII:
  • Language:
  • Keywords:
    IGS, Liver surgery, Simulation
  • Notes:
    This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
PURPOSE: The use of synthetic or simulated data has the potential to greatly improve the availability and volume of training data for image guided surgery and other medical applications, where access to real-life training data is limited. METHODS: By using the Unity game engine, complex intraoperative scenes can be simulated. The Unity Perception package allows for randomisation of paremeters within the scene, and automatic labelling, to make simulating large data sets a trivial operation. In this work, the approach has been prototyped for liver segmentation from laparoscopic video images. 50,000 simulated images were used to train a U-Net, without the need for any manual labelling. The use of simulated data was compared against a model trained with 950 manually labelled laparoscopic images. RESULTS: When evaluated on data from 10 separate patients, synthetic data outperformed real data in 4 out of 10 cases. Average DICE scores across the 10 cases were 0.59 (synthetic data), 0.64 (real data) and 0.75 (both synthetic and real data). CONCLUSION: Synthetic data generated using this method is able to make valid inferences on real data, with average performance slightly below models trained on real data. The use of the simulated data for pre-training boosts model performance, when compared with training on real data only.
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Dept of Med Phys & Biomedical Eng
Department of Surgical Biotechnology
Dept of Med Phys & Biomedical Eng
Department of Surgical Biotechnology
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