The algorithm was tested on datasets acquired with a Nikon A1 confocal microscope (40x objective, excitation length 457 nm, bandwidth 500–550 nm), and a LEICA SP8 STED 3DX (93x objective, pulsed white-light laser 598 nm, pulsed 775 nm depletion laser, bandwidth 605–777 nm). All image stacks were from Purkinje cells within murine cerebella, cleared as in Magliaro et al.30 (link). We evaluated SENPAI’s performance in distinguishing both cellular (i.e., neurons) and subcellular (i.e., spines) structures.
The first 40x confocal image is a 512-by-512-by-143 image containing 114 somas (Fig.2A ), while the second one is a 512-by-512-by-139 image containing 103 somas. SENPAI was used on these datasets with a single clustering instance, without Gaussian smoothing. Then, post-processing routines including parcellation and pruning were exploited for isolating single cells.
The 93x STED dataset is a 1024-by-1024-by-35 image in which several sections of neurons can be observed (Fig.2A ). The segmentation with SENPAI was obtained merging two clustering instances with different levels of smoothness (i.e., setting the standard deviations of the 3D Gaussian filter to 0 and 3). Finally, we assigned the dendritic spines to their parent branch.
The first 40x confocal image is a 512-by-512-by-143 image containing 114 somas (Fig.
The 93x STED dataset is a 1024-by-1024-by-35 image in which several sections of neurons can be observed (Fig.
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