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

TL;DR

This paper introduces a deep learning-based method using the Segment Anything Model for objective, reproducible quantification of wound healing in vitro scratch assays, outperforming traditional semi-automatic methods.

Contribution

It demonstrates the application of a foundation model for domain-agnostic segmentation in biological assays, reducing subjectivity and variability without domain-specific training.

Findings

Outperforms semi-objective baseline method

Shows very low intra- and interobserver variability

Achieves objective and reproducible segmentation

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.