Machine Learning identifies remodeling patterns in human lung extracellular matrix.

Organ function depends on the three-dimensional integrity of the extracellular matrix (ECM). The structure resulting from the location and association of ECM components is a central regulator of cell behavior, but a dearth of matrix-specific analysis keeps it unresolved. Here, we deploy a high-resolution, 3D ECM mapping method and design a machine-learning powered pipeline to detect and characterize ECM architecture during health and disease.

A multimodal data-set of a unidirectional glass fibre reinforced polymer composite.

A unidirectional (UD) glass fibre reinforced polymer (GFRP) composite was scanned at varying resolutions in the micro-scale with several imaging modalities. All six scans capture the same region of the sample, containing well-aligned fibres inside a UD load-carrying bundle. Two scans of the cross-sectional surface of the bundle were acquired at a high resolution, by means of scanning electron microscopy (SEM) and optical microscopy (OM), and four volumetric scans were acquired through X-ray computed tomography (CT) at different resolutions.