Name
Title | ||
|---|---|---|
Grid Screener: A Tool for Automated High-Throughput Screening on Biochemical and Biological Analysis Platforms |
Abstract | ||
|---|---|---|
Grid structures are common in high-throughput assays to parallelize experiments in biochemical or biological experiments. Manual analysis of grid images is laborious, time-consuming, expensive, and critical in terms of reproducibility. However, it is still common to do such analysis manually, as there is no standardized software for automated analysis. In this paper, we introduce a generic method to automatically detect grid structures in images and to perform flexible spot-wise analysis after successful grid detection. The deep learning-based approach of the grid structure detection allows being flexible concerning different grid types. The combination with a robust parameter estimation algorithm lowers the requirements of the detection quality and thus enhances robustness. Further, the method conducts semi-automated grid detection if a fully automated processing fails. An open-source software tool Grid Screener that implements the proposed methods is provided as a ready-for-use tool for researchers. The usability is demonstrated by taking different criteria into account, which are important for a successful application. We present the benefits of our proposed tool Grid Screener utilizing three different grid types in the context of high-throughput screening to show our contribution towards further lab automation. Our tool performs much faster than manual analysis, while maintaining or even enhancing accuracy. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3135709 | IEEE ACCESS |
Keywords | DocType | Volume |
Biology, Shape, Image processing, Estimation, Pipelines, Image segmentation, Image color analysis, Application software, artificial neural networks, automation, biological systems, chemical technology, machine learning, parameter estimation, image processing | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcel P. Schilling | 1 | 0 | 0.34 |
| Svenja Schmelzer | 2 | 0 | 0.34 |
| Joaquin Eduardo Urrutia Gomez | 3 | 0 | 0.34 |
| Anna A. Popova | 4 | 0 | 0.34 |
| Pavel A. Levkin | 5 | 0 | 0.34 |
| Markus Reischl | 6 | 0 | 0.34 |