U-Net Deep-Learning-Based 3D Cell Counter for the Quality Control of 3D Cell-Based Assays through Seed Cell Measurement
| Autor: | Eun Ji Jeong, Dong Woo Lee, Donghyuk Choi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Quality Control
Computer science media_common.quotation_subject Cell 03 medical and health sciences 3D cell culture 0302 clinical medicine Software Deep Learning medicine Quality (business) 030304 developmental biology media_common 0303 health sciences business.industry Deep learning Reproducibility of Results Cell counting Cell based assays Computer Science Applications Medical Laboratory Technology medicine.anatomical_structure Artificial intelligence business 030217 neurology & neurosurgery Computer hardware Algorithms |
| Zdroj: | SLAS technology. 26(5) |
| ISSN: | 2472-6311 |
| Popis: | Conventional cell-counting software uses contour or watershed segmentations and focuses on identifying two-dimensional (2D) cells attached on the bottom of plastic plates. Recently developed software has been useful tools for the quality control of 2D cell-based assays by measuring initial seed cell numbers. These algorithms do not, however, quantitatively test in three-dimensional (3D) cell-based assays using extracellular matrix (ECM), because cells are aggregated and overlapped in the 3D structure of the ECM such as Matrigel, collagen, and alginate. Such overlapped and aggregated cells make it difficult to segment cells and to count the number of cells accurately. It is important, however, to determine the number of cells to standardize experiments and ensure the reproducibility of 3D cell-based assays. In this study, we apply a 3D cell-counting method using U-net deep learning to high-density aggregated cells in ECM to identify initial seed cell numbers. The proposed method showed a 10% counting error in high-density aggregated cells, while the contour and watershed segmentations showed 30% and 40% counting errors, respectively. Thus, the proposed method can reduce the seed cell-counting error in 3D cell-based assays by providing the exact number of cells to researchers, thereby enabling the acquisition of quality control in 3D cell-based assays. |
| Databáze: | OpenAIRE |
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