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An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images

  • Image & Signal Processing
  • Published:
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Abstract

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

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Acknowledgements

We thank Dr. Tatsuaki Etou and Kensuke Yamada for his expert technical assistance in running the computer program. We also acknowledge Editage (www.editage.jp) for English language editing.

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Correspondence to Shinichi Hashimoto.

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The authors declare that they have no conflicts of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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This article is part of the Topical Collection on Image & Signal Processing

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Hashimoto, S., Ogihara, H., Suenaga, M. et al. An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images. J Med Syst 41, 119 (2017). https://doi.org/10.1007/s10916-017-0769-5

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  • DOI: https://doi.org/10.1007/s10916-017-0769-5

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