Journal
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2021.797555
Keywords
foreign body response; deep learning; U-Net; intravital multiphoton microscopy; image analysis
Funding
- David H. Koch Center for Applied Research of Genitourinary Cancers and Bayer HealthCare Pharmaceuticals [57440]
- NIH/NCI [P30 CA016672]
- Regional Operative Program-European Fund for Regional Development 2014-2020-Lombardia Region [ARIA_2020_403, 138.360.852]
- NIH/NIBIB [P41 EB023833]
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This study developed an integrated computational pipeline based on an innovative variant of the U-Net neural network to segment and quantify cellular and extracellular structures of interest. The software shows promise for automatically detecting the elements of the FBR, unraveling the complexity of this pathophysiological process.
The Foreign body response (FBR) is a major unresolved challenge that compromises medical implant integration and function by inflammation and fibrotic encapsulation. Mice implanted with polymeric scaffolds coupled to intravital non-linear multiphoton microscopy acquisition enable multiparametric, longitudinal investigation of the FBR evolution and interference strategies. However, follow-up analyses based on visual localization and manual segmentation are extremely time-consuming, subject to human error, and do not allow for automated parameter extraction. We developed an integrated computational pipeline based on an innovative and versatile variant of the U-Net neural network to segment and quantify cellular and extracellular structures of interest, which is maintained across different objectives without impairing accuracy. This software for automatically detecting the elements of the FBR shows promise to unravel the complexity of this pathophysiological process.
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