| RRC ID |
73017
|
| Author |
Debnath T, Hattori R, Okamoto S, Shibata T, Santra TS, Nagai M.
|
| Title |
Automated detection of patterned single-cells within hydrogel using deep learning.
|
| Journal |
Sci Rep
|
| Abstract |
Single-cell analysis has been widely used in various biomedical engineering applications, ranging from cancer diagnostics, and immune response monitoring to drug screening. Single-cell isolation is fundamental for observing single-cell activities and an automatic finding method of accurate and reliable cell detection with few possible human errors is also essential. This paper reports trapping single cells into photo patternable hydrogel microwell arrays and isolating them. Additionally, we present an object detection-based DL algorithm that detects single cells in microwell arrays and predicts the presence of cells in resource-limited environments at the highest possible mAP (mean average precision) of 0.989 with an average inference time of 0.06 s. This algorithm leads to the enhancement of the high-throughput single-cell analysis, establishing high detection precision and reduced experimentation time.
|
| Volume |
12(1)
|
| Pages |
18343
|
| Published |
2022-10-31
|
| DOI |
10.1038/s41598-022-22774-0
|
| PII |
10.1038/s41598-022-22774-0
|
| PMID |
36316380
|
| PMC |
PMC9622733
|
| MeSH |
Deep Learning*
Humans
Hydrogels*
|
| IF |
3.998
|
| Resource |
| Human and Animal Cells |
HeLa(RCB0007) |
