Self-supervised Visual Place Recognition for Colonoscopy SequencesDownload PDF

Apr 08, 2021 (edited Apr 20, 2021)MIDL 2021 Conference Short SubmissionReaders: Everyone
  • Keywords: Deep Learning, Place Recognition, CNN, Colonoscopy
  • TL;DR: We propose the first place recognition system made for colonoscopy sequences, with the use of real colonoscopy image pairs for training obtained in an automated manner.
  • Abstract: We present the first place recognition system trained specifically for colonoscopy sequences. We use the convolutional neural network for image retrieval proposed by Radenovic et al. and we fine-tune it using image pairs from real human colonoscopies. The colonoscopy frames are clustered automatically by a Structure-from-Motion (SfM) algorithm, which has proven to cope with scene deformation and illumination changes. The experiments show that the system is able to generalize by testing in a different human colonoscopy, retrieving frames observing the same place despite of the different viewpoint and illumination changes. The proposed place recognition would be a key component of Simultaneous Localization and Mapping (SLAM) systems operating in colonoscopy to assist doctors during the explorations or to support robotization.
  • Paper Type: validation/application paper
  • Primary Subject Area: Unsupervised Learning and Representation Learning
  • Secondary Subject Area: Application: Endoscopy
  • Paper Status: original work, not submitted yet
  • Source Code Url: This is a preliminary study yet so the code is not suitable for public release. It is our intention to release the code and the trained model in the future.
  • Data Set Url: The colonoscopy data used for the experiments make secondary use of real human colonoscopies, the ethical approval does not allow to make these recordings publicly available.
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  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
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