![]() ![]() Even for experts, it is often easier to annotate new images instead of adjusting parameters for low-resolution and low signal-to-noise-ratio data. In addition, the parameters of sophisticated analysis pipelines need to be adjusted in case of changes in the experimental design. The complexity of bead detection in low-resolution images may lead biologists to either use an inefficient and user-biased manual quantification or to use a user-friendly but inaccurate method. ![]() The predictions of BeadNet are shown in ( e) (white in gray ground truth) The white 2 × 2 px seeds in ( d) are enlarged (gray) for the calculation of the evaluation metrics. The upsampled test image is shown in ( c). The 32 × 32 px test image in ( b) is taken from the red fluorescent channel of (a). ( a) A maximum intensity projection of red fluorescent beads (additional green fluorophore for beads outside of cells) and blue fluorescent cell nuclei. ![]() Exemplary application case and detection results of BeadNet. ![]()
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