Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images

• Published in: Frontiers in bioengineering and biotechnology Vol. 9; p. 797555
• Main Authors: Sarti, Mattia, Parlani, Maria, Diaz-Gomez, Luis, Mikos, Antonios G., Cerveri, Pietro, Casarin, Stefano, Dondossola, Eleonora
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 25.01.2022
• Subjects: Bioengineering and Biotechnology; deep learning; foreign body response; image analysis; intravital multiphoton microscopy; U-Net; deep learning; intravital multiphoton microscopy; foreign body response; image analysis; U-Net
• ISSN: 2296-4185; 2296-4185
• DOI: 10.3389/fbioe.2021.797555

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.