OFDVDnet: A Sensor Fusion Approach for Video Denoising in Fluorescence-Guided Surgery

Trevor Seets, Wei Lin, Yizhou Lu, Christie Lin, Adam Uselmann, Andreas Velten
Medical Imaging with Deep Learning, PMLR 227:1564-1580, 2024.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v227-seets24a, title = {OFDVDnet: A Sensor Fusion Approach for Video Denoising in Fluorescence-Guided Surgery}, author = {Seets, Trevor and Lin, Wei and Lu, Yizhou and Lin, Christie and Uselmann, Adam and Velten, Andreas}, booktitle = {Medical Imaging with Deep Learning}, pages = {1564--1580}, year = {2024}, editor = {Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit}, volume = {227}, series = {Proceedings of Machine Learning Research}, month = {10--12 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v227/seets24a/seets24a.pdf}, url = {https://proceedings.mlr.press/v227/seets24a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T OFDVDnet: A Sensor Fusion Approach for Video Denoising in Fluorescence-Guided Surgery %A Trevor Seets %A Wei Lin %A Yizhou Lu %A Christie Lin %A Adam Uselmann %A Andreas Velten %B Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ipek Oguz %E Jack Noble %E Xiaoxiao Li %E Martin Styner %E Christian Baumgartner %E Mirabela Rusu %E Tobias Heinmann %E Despina Kontos %E Bennett Landman %E Benoit Dawant %F pmlr-v227-seets24a %I PMLR %P 1564--1580 %U https://proceedings.mlr.press/v227/seets24a.html %V 227 %X 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.
APA
Seets, T., Lin, W., Lu, Y., Lin, C., Uselmann, A. & Velten, A.. (2024). OFDVDnet: A Sensor Fusion Approach for Video Denoising in Fluorescence-Guided Surgery. Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 227:1564-1580 Available from https://proceedings.mlr.press/v227/seets24a.html.

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