LIVECell—A large-scale dataset for label-free live cell segmentation
| Autor: | Timothy R Jackson, Sheraz Ahmed, Nicola Bevan, Timothy Dale, Johan Trygg, Christoffer Edlund, Nabeel Khalid, Andreas Dengel, Rickard Sjögren |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Resource
Technology Databases Factual Computer science Cell Culture Techniques ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Models Biological Biochemistry Convolutional neural network Field (computer science) Set (abstract data type) Image Processing Computer-Assisted Humans Segmentation Molecular Biology Microscopy Artificial neural network business.industry Deep learning Medicinsk bildbehandling Pattern recognition Cell Biology Image segmentation Research data Medical Image Processing Neural Networks Computer Artificial intelligence business Biotechnology |
| Zdroj: | Nature Methods |
| ISSN: | 1548-7105 1548-7091 |
| DOI: | 10.1038/s41592-021-01249-6 |
| Popis: | Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks. The LIVECell dataset comprises annotated phase-contrast images of over 1.6 million cells from different cell lines during growth from sparse seeding to confluence for improved training of deep learning-based models of image segmentation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink