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. 2016 Feb 23;16:144. doi: 10.1186/s12885-016-2194-4

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© Nolan et al. 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 2 — The decision tree diagram used for analyzing 3D-OCT images. A brief, sample training image set and this decision tree were developed for providing the blinded readers with direction for identifying native and abnormal lymph node anatomical structure, as well as possible image artifacts from imaging limitations in surgery. Each trained reader classified the OCT datasets as either “metastatic” or “non-metastatic”