Cell detection on image-based immunoassays

TL;DR

This paper introduces a PDE-based mathematical model for cell detection in image-based immunoassays, providing a new objective methodology that performs comparably to human experts in real data.

Contribution

The paper develops a PDE-inspired cell detection method for immunoassay images, addressing the lack of objective ground truth and improving automation.

Findings

Method performs comparably to human experts

Mathematical model offers an objective detection approach

Applicable to ELISPOT, Fluorospot, and similar assays

Abstract

Cell detection and counting in the image-based ELISPOT and Fluorospot immunoassays is considered a bottleneck. The task has remained hard to automatize, and biomedical researchers often have to rely on results that are not accurate. Previously proposed solutions are heuristic, and data-based solutions are subject to a lack of objective ground truth data. In this paper, we analyze a partial differential equations model for ELISPOT, Fluorospot, and assays of similar design. This leads us to a mathematical observation model for the images generated by these assays. We use this model to motivate a methodology for cell detection. Finally, we provide a real-data example that suggests that this cell detection methodology and a human expert perform comparably.