Fig. 4: Machine learning-based multicolor imaging of glioblastoma, blood vessels and white matter tracts.

a Scheme for customized machine learning based classification of THG signal into myelin and vessel signal. b Distribution of uncertainty level with machine learning compared with customized machine learning (left) and statistical comparison of uncertainty levels (n = 239568 THG pixels, two-sided Mann–Whitney test, shown as median +/- quartile, whiskers: min/max within 1.5 IQR). c Exemplary image of THG signal without (top) and with (bottom) predicted labels of blood vessels and myelin fibers. Arrowheads indicate blood vessels; arrow indicates myelin fibers (n = 3 experiments with similar results). d 3D rendering within the CC, illustrating the results of the machine learning-based classification. Arrowhead indicates vessels, arrow indicates myelin fibers. e Validation of ML-classification for blood vessels with FITC as fluorescent dye colored in green. The arrowheads point at vessels (n = 3 experiments with similar results). f Comparison of high and low FITC signal with THG signal (n = 62244 pixels, two-sided Mann–Whitney test, shown as median +/- quartile, whiskers: min/max within 1.5 IQR). g Histogram of measured blood vessels based on their diameter and colored based on their identification from THG signal (blue: visible with FITC and in THG signal, green: visible only with FITC, n = 68 vessels). h UMAP based on pixel features that are different between background, myelin, and vessels based on the machine learning prediction (n = 100 features). Pixels are colored based on the local frequency of pixels in the dimensionality-reduced space (n = 239568 pixels). i Close-up 3D rendering of a single GBMC (green) and its surrounding microenvironment (vessel in red, myelin in blue). The asterisk points at a vessel branching points, the arrow at a TM branching point and the arrowhead at a glioblastoma small process. Gamma values were adjusted for 3D-visualization in (d, i). Source data are provided as a Source Data file.