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

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.

SEEK ID: https://seek.lisym.org/publications/306

DOI: 10.1109/ICIP.2019.8803057

Projects: LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI)

Publication type: InProceedings

Journal: 2019 IEEE International Conference on Image Processing (ICIP)

Book Title: 2019 IEEE International Conference on Image Processing (ICIP)

Publisher: IEEE

Citation: 2019 IEEE International Conference on Image Processing (ICIP),pp.1361-1365,IEEE

Date Published: 1st Sep 2019

Registered Mode: by DOI

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Citation
Morales-Navarrete, H., Segovia-Miranda, F., Zerial, M., & Kalaidzidis, Y. (2019). Prediction of Multiple 3D Tissue Structures Based on Single-Marker Images Using Convolutional Neural Networks. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 1361–1365). IEEE. https://doi.org/10.1109/icip.2019.8803057
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Created: 23rd Jun 2021 at 08:07

Last updated: 8th Mar 2024 at 07:44

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