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Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation

Pfeiffer, M; Funke, I; Robu, MR; Bodenstedt, S; Strenger, L; Engelhardt, S; Roß, T; ... Speidel, S; + view all (2019) Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation. In: Proceedings of the Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation. (pp. pp. 119-127). Springer: Shenzhen, China. Green open access

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Abstract

In the medical domain, the lack of large training data sets and benchmarks is often a limiting factor for training deep neural networks. In contrast to expensive manual labeling, computer simulations can generate large and fully labeled data sets with a minimum of manual effort. However, models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real laparoscopic images, we exploit recent advances in unpaired image-to-image translation. We extent an image-to-image translation method to generate a diverse multitude of realistically looking synthetic images based on images from a simple laparoscopy simulation. By incorporating means to ensure that the image content is preserved during the translation process, we ensure that the labels given for the simulated images remain valid for their realistically looking translations. This way, we are able to generate a large, fully labeled synthetic data set of laparoscopic images with realistic appearance. We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images. We achieve average dice scores of up to 0.89 in some patients without manually labeling a single laparoscopic image and show that using our synthetic data to pre-train models can greatly improve their performance. The synthetic data set will be made publicly available, fully labeled with segmentation maps, depth maps, normal maps, and positions of tools and camera (http://opencas.dkfz.de/image2image).

Type: Proceedings paper
Title: Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
Event: International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2019
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-32254-0_14
Publisher version: https://doi.org/10.1007/978-3-030-32254-0_14
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Unsupervised, Image Translation, Segmentation, Laparoscopy
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Surgical Biotechnology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10077811
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