Detalle de: A Cell Segmentation/Tracking Tool Based on Machine Learning
| Title | A Cell Segmentation/Tracking Tool Based on Machine Learning |
| Abstract |
The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji's Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol. |
| Apa Reference | Deter, H.S., Dies, M., Cameron, C.C., Butzin, N.C. & Buceta, J. (2019). A Cell Segmentation/Tracking Tool Based on Machine Learning. Methods in Molecular Biology, 399-422. 10.1007/978-1-4939-9686-5_19 |
| Doi | 10.1007/978-1-4939-9686-5_19 |
| Publication Date | 2019 |
| Publication Type | Software |
| Researcher | Javier Buceta |
| Ontology | Artificial Intelligence, Machine Learning in Biology, Bioinformatics Software and Tools, Image analysis, Single-cell omics |
| Research Groups | |
| Tools | Bacteria Segmentation/Tracking Tool |