Prediction of Multiple 3D Tissue Structures Based on Single-Marker Images Using Convolutional Neural Networks.

First Authors Hernán Morales-Navarrete
Authors Hernán Morales-Navarrete, Fabián Segovia-Miranda, Marino Zerial, Yannis Kalaidzidis
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Last Authors Yannis Kalaidzidis
Conference Proceedings Volume Title 2019 IEEE International Conference on Image Processing : proceedings : September 22-25, 2019, Taipei International Convention Center (TICC), Taipei, Taiwan
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Conference Name 26th IEEE International Conference on Image Processing (ICIP)
Conference Location Taipei, Taiwan
Conference Start Date 2019-09-22
Conference End Date 2019-09-25
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Publisher IEEE
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ISBN 978-1-5386-6249-6
First Page 1361
Last Page 1365
Print Publication Date 2019-09-26
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Abstract A quantitative understanding of complex biological systems such as tissues requires reconstructing the structure of the different components of the system. Fluorescence microscopy provides the means to visualize simultaneously several tissue components. However, it can be time consuming and is limited by the number of fluorescent markers that can be used. In this study, we describe a toolbox of algorithms based on convolutional neural networks for the prediction of 3D tissue structures by learning features embedded within single-marker images. As proof of principle, we aimed to predict the network of bile canaliculi (BC) in liver tissue using images of the cortical actin mesh as input. The actin meshwork has a characteristic organization in specific cellular domains, such as BC. However, the use of manually selected features from images of actin is not sufficient to properly reconstruct BC structure. Our deep learning framework showed a remarkable accuracy for the prediction of BC network and was successfully adapted (i.e. transfer learning) to predict the sinusoidal network. This approach allows for a complete reconstruction of tissue microarchitecture using a single fluorescent marker.
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Affiliated With Predoc first author, Zerial
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DOI 10.1109/ICIP.2019.8803057
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Created By thuem
Added Date 2020-04-17
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Last Edited Date 2020-05-19 14:24:17.451
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