Fig. 3: Aggregate detection pipeline.

a, A typical sample image containing features of various sizes and intensities, that is, Lewy neurites, micron-scale aggregates and sub-diffraction-limit oligomers. b, The aggregate detection pipeline for measuring large aggregates (top) and subdiffraction-sized features (bottom). The large object pipeline combines the z-projected intensity data with background-subtracted and threshold individual slices to generate a binary mask. Small aggregates are identified using a Ricker wavelet filter that acts as a bandpass, emphasizing small spots, which are then measured with a threshold and sorted by the number of pixels above the background. Features larger than the diffraction limit are reclassified as ‘large’ and features overlapping between the two masks are removed from the small aggregate pool. c, The large (green) and oligomer (blue) masks shown over the original image. d, The representative oligomers detected from c. e–h, The quantification of the pipeline performance using simulated images of diffraction-limited spots on a noisy background at various signal-to-noise ratios. The grey shaded region represents the lower quartile determined from experimental conditions, while the green and pink shaded area represents the mean ± s.d. The intensity and average background values for all detected peaks in simulated images were estimated by quantifying the pixel values around the detected peaks (pink curve) and by fitting a symmetric two-dimensional-Gaussian function with nonlinear least squares fitting (green). The presented values were obtained by averaging the mean and s.d. within the IQR of the experimental CNR data (Q1 = 4.2, Q3 = 8.1).