Fig. 4.

Random forest classifier outperforms auto-thresholding in segmenting a string phantom. XY maximum intensity projections of string phantom imaged with RSOM show that random forest-based segmentation outmatches auto-thresholding when correcting for depth-dependent SNR. (A) Photoacoustic mesoscopy (RSOM) image shows measured string PA signal intensity with top (0.5 mm), middle (1 mm) and bottom (2 mm) strings labelled. (B) Binary masks are shown following segmentation using: (AT) auto-thresholding; (RF) Random forest classifier; (AT+VF) vesselness filtered strings with auto-thresholding; and (RF+VF) vesselness filtered strings with random-forest classifier. (C) Skeletonised string volume calculated from segmented images of 3 strings placed at increasing depths in an agar phantom. Results from all 4 segmentation pipelines are shown. All volume comparisons (top vs. middle, top vs. bottom, middle vs. bottom) where significant (p < 0.05) except middle vs. bottom for RF+VF (p = 0.42). (D) SNR decreases with increasing depth. (E) Illumination geometry: known cross-section of string outlined (left); during measurement, signal is detected from the partially illuminated section (outlined) resulting in an underestimation in string volume (right). (F) String volume calculated pixel-wise from the segmented binary mask. (C,D,F) Data represented by truncated violin plots with interquartile range (bold) and median (dotted), * ** *=p < 0.0001 (n = 7 scans). (C,F) Dotted line indicates ground truth volume 0.105 mm³. See Supplementary Movie 2 for 3D visualisation.