Virtually Objective Quantification of in vitro Wound Healing Scratch Assays with the Segment Anything Model
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
CCS Concepts: Computing methodologies → Image segmentation; Applied computing → Imaging
@inproceedings{10.2312:vcbm.20241186,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Garrison, Laura and Jönsson, Daniel},
title = {{Virtually Objective Quantification of in vitro Wound Healing Scratch Assays with the Segment Anything Model}},
author = {Löwenstein, Katja and Rehrl, Johanna and Schuster, Anja and Gadermayr, Michael},
year = {2024},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-244-8},
DOI = {10.2312/vcbm.20241186}
}