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arXiv · 2407.02187

Virtually Objective Quantification of in vitro Wound Healing Scratch Assays with the Segment Anything Model

Katja Löwenstein·Johanna Rehrl·Anja Schuster·Michael Gadermayr
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Abstract

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.

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Katja Löwenstein, Johanna Rehrl, Anja Schuster, Michael Gadermayr. 2024-07-02. Virtually Objective Quantification of in vitro Wound Healing Scratch Assays with the Segment Anything Model. https://arxiv.org/abs/2407.02187

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