Fig. 3: Denoising of AO-3PM and subsequent machine learning allows brain tumor imaging across the entire cortex and corpus callosum.

a Top left: 3D rendering of a stack going from the surface down to the CC. Red: blood vessels, blue: CC, green: GBMCs. Based on probability maps. Top right: Comparison of raw (left) and denoised (right) images within the CC (dashed lines on the left indicate imaging depth). Arrowheads in the mGFP image point to a GBMC soma that can be barely seen without denoising. Arrowheads in the THG signal point to fibrous structures that can be clearly identified after denoising. Bottom: signal-to-noise ratio (SNR) comparison of raw and denoised in THG and mGFP signal (two-sided Mann-Whitney test, n = 114 slices for each mGFP and THG signal, shown as median +/- quartile, whiskers: min/max within 1.5 IQR). b Left: Exemplary raw and denoised images of GBMCs at different depths. Right: SNR in raw and denoised images across entire image stack. (n = 6 experiments with similar results) c Comparison of the denoised 3PM-N2V image (left) and its version with additional application of the PerStruc-Denoiser (middle) showing the qualitative improvement corresponding to a 3 dB increase in SNR allowing a clearer identification of TMs (arrowhead). Arrows point to structured noise. Right: Averaged line power spectrum of the images depicting the PerStruc-Denoiser’s suppression of the main components of the periodic structured noise (see arrows pointing to its main components). d 3D renderings based on raw images, denoised images, probability maps based on raw images and probability maps based on denoised images. e Close-up 3D renderings of single GBMCs based on probability maps. The arrow heads on the zoom-ins point at small processes (top images and bottom left image) and a TM branching point (bottom right image). Gamma values were adjusted for 3D visualization in (a, d, e). Source data are provided as a Source Data file.