The MMTM algorithm reliably detected vesicles across a wide range of concentrations, from abundant to sparse. It also distinguished vesicles from non-vesicular structures such as crystalline formations and oil droplets, reducing the likelihood of false positives. For example, the algorithm correctly excludes oil droplets (solid round shapes in Figure ) and crystalline shapes (long strips in Figure ). A complete analysis of the 768 images in a batch of experiments requires only 84 minutes of CPU time on a laptop computer and is trivially parallelized. This is much faster than human experts. Although the present study only required counting vesicles, the algorithm can also calculate vesicle sizes and automate preprocessing and post-processing tasks, including the recording of individual image analyses and compiling summary results for entire batches. This efficiency accelerates the pace of research and enhances reproducibility by minimizing human error.