  The in vitro scratch assay is a widely used assay in cell biology to assess
the rate of wound closure related to a variety of therapeutic interventions.
While manual measurement is subjective and vulnerable to intra- and
interobserver variability, computer-based tools are theoretically objective,
but in practice often contain parameters which are manually adjusted
(individually per image or data set) and thereby provide a source for
subjectivity. Modern deep learning approaches typically require large annotated
training data which complicates instant applicability. In this paper, we make
use of the segment anything model, a deep foundation model based on interactive
point-prompts, which enables class-agnostic segmentation without tuning the
network's parameters based on domain specific training data. The proposed
method clearly outperformed a semi-objective baseline method that required
manual inspection and, if necessary, adjustment of parameters per image. Even
though the point prompts of the proposed approach are theoretically also a
source for subjectivity, results attested very low intra- and interobserver
variability, even compared to manual segmentation of domain experts.
