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Tytuł:
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RNNSLAM: Reconstructing the 3D colon to visualize missing regions during a colonoscopy.
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Autorzy:
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Ma R; University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA. Electronic address: .
Wang R; University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA.
Zhang Y; University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA.
Pizer S; University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA.
McGill SK; University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA.
Rosenman J; University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA.
Frahm JM; University of North Carolina at Chapel Hill, Chapel Hill, NC 27705, USA.
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Źródło:
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Medical image analysis [Med Image Anal] 2021 Aug; Vol. 72, pp. 102100. Date of Electronic Publication: 2021 May 19.
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Typ publikacji:
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Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Publication: Amsterdam : Elsevier
Original Publication: London : Oxford University Press, [1996-
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MeSH Terms:
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Colonic Polyps*/diagnostic imaging
Colonoscopy*
Colon/diagnostic imaging ; Humans
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References:
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Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2018 Jun;2018:2002-2011. (PMID: 31274971)
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2020 Jun;2020:4695-4704. (PMID: 33456298)
IEEE Trans Pattern Anal Mach Intell. 2019 Oct 14;:. (PMID: 31613751)
IEEE Trans Med Imaging. 2014 Jan;33(1):135-46. (PMID: 24107925)
IEEE Trans Pattern Anal Mach Intell. 2018 Mar;40(3):611-625. (PMID: 28422651)
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Grant Information:
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R01 CA158925 United States CA NCI NIH HHS
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Contributed Indexing:
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Keywords: Colonoscopy; Missing region; Recurrent neural network; SLAM
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Entry Date(s):
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Date Created: 20210608 Date Completed: 20210802 Latest Revision: 20220802
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Update Code:
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20240104
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PubMed Central ID:
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PMC8316389
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DOI:
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10.1016/j.media.2021.102100
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PMID:
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34102478
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Colonoscopy is the gold standard for pre-cancerous polyps screening and treatment. The polyp detection rate is highly tied to the percentage of surveyed colonic surface. However, current colonoscopy technique cannot guarantee that all the colonic surface is well examined because of incomplete camera orientations and of occlusions. The missing regions can hardly be noticed in a continuous first-person perspective. Therefore, a useful contribution would be an automatic system that can compute missing regions from an endoscopic video in real-time and alert the endoscopists when a large missing region is detected. We present a novel method that reconstructs dense chunks of a 3D colon in real time, leaving the unsurveyed part unreconstructed. The method combines a standard SLAM system with a depth and pose prediction network to achieve much more robust tracking and less drift. It addresses the difficulties for colonoscopic images of existing simultaneous localization and mapping (SLAM) systems and end-to-end deep learning methods.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2021 Elsevier B.V. All rights reserved.)