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Editors contains: "Landman, Bennett"

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  1. ; ; ; ; ; (, SPIE Medical Imaging Symposium)
    Landman, Bennett A.; Išgum, Ivana (Ed.)
    Full Text Available 
  2. ; ; ; ; ; ; ; ; ; ; et al (, SPIE Medical Imaging)
    Landman, Bennett A.; Išgum, Ivana (Ed.)
    Full Text Available 
  3. ; ; ; ; ; (, Proceedings of Machine Learning Research)
    Oguz, Ipek; Noble, Jack; Li, Xiaoxiao; Styner, Martin; Baumgartner, Christian; Rusu, Mirabela; Heinmann, Tobias; Kontos, Despina; Landman, Bennett; Dawant, Benoit (Ed.)
    Many applications in machine vision and medical imaging require the capture of images from a scene with very low radiance, which may result in very noisy images and videos. An important example of such an application is the imaging of fluorescently-labeled tissue in fluorescence-guided surgery. Medical imaging systems, especially when intended to be used in surgery, are designed to operate in well-lit environments and use optical filters, time division, or other strategies that allow the simultaneous capture of low radiance fluorescence video and a well-lit visible light video of the scene. This work demonstrates video denoising can be dramatically improved by utilizing deep learning together with motion and textural cues from the noise-free video. 
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    Accepted Manuscript Full Text Available