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2021 | OriginalPaper | Buchkapitel

FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos

verfasst von : Shawn Mathew, Saad Nadeem, Arie Kaufman

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

Verlag: Springer International Publishing

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Abstract

Haustral folds are colon wall protrusions implicated for high polyp miss rate during optical colonoscopy procedures. If segmented accurately, haustral folds can allow for better estimation of missed surface and can also serve as valuable landmarks for registering pre-treatment virtual (CT) and optical colonoscopies, to guide navigation towards the anomalies found in pre-treatment scans. We present a novel generative adversarial network, FoldIt, for feature-consistent image translation of optical colonoscopy videos to virtual colonoscopy renderings with haustral fold overlays. A new transitive loss is introduced in order to leverage ground truth information between haustral fold annotations and virtual colonoscopy renderings. We demonstrate the effectiveness of our model on real challenging optical colonoscopy videos as well as on textured virtual colonoscopy videos with clinician-verified haustral fold annotations. All code and scripts to reproduce the experiments of this paper will be made available via our Computational Endoscopy Platform at https://github.com/nadeemlab/CEP.

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Supplementary Video: https://youtu.be/_iWBJnDMXjo.

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Titel: FoldIt: Haustral Folds Detection and Segmentation in Colonoscopy Videos
verfasst von: Shawn Mathew
Saad Nadeem
Arie Kaufman
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Print ISBN: 978-3-030-87198-7

Electronic ISBN: 978-3-030-87199-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-87199-4_21

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