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Cited by 304Cell Image Analysis TechniquesAdvanced Neural Network ApplicationsMedical Image Segmentation Techniques

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LIVECell—A large-scale dataset for label-free live cell segmentation

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Edlund, Christoffer, Jackson, Timothy R., Khalid, Nabeel, Bevan, Nicola, Dale, Timothy, Dengel, Andreas, Ahmed, Sheraz, Trygg, Johan, Sjögren, Rickard·2021·Nature Methods
DOI 10.1038/s41592-021-01249-6PMC PMC8440198
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Abstract

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.

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Crossref · 10.1038/s41592-021-01249-6

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Figures

Fig. 1 Morphological diversity of cell types comprising LIVECell visualized using PCA. a, Scatter plot of the first two principal components demonstrate the diverse spread of morphologies represented by images in LIVECell between and within cell types. b, Loading plot of PCA shows how each morphology metrics influence the directions of the component values. c, Representative examples of images on each axis and quadrant of the principal component plot with their principal component values plotted. Morphological interpretations, based on the loading values, are provided for each quadrant. Abbreviated metrics names are explained in the Methods. Scale bar represents 100 µm and applies to all images.
Fig. 1 Morphological diversity of cell types comprising LIVECell visualized using PCA. a, Scatter plot of the first two principal components demonstrate the diverse spread of morphologies represented by images in LIVECell between and within cell types. b, Loading plot of PCA shows how each morphology metrics influence the directions of the component values. c, Representative examples of images on each axis and quadrant of the principal component plot with their principal component values plotted. Morphological interpretations, based on the loading values, are provided for each quadrant. Abbreviated metrics names are explained in the Methods. Scale bar represents 100 µm and applies to all images.CC BY · PubMed Central
Fig. 2 Illustrative examples of annotated phase-contrast microscopy images and histograms showing cell size distributions of all cell types in LIVECell. Example images for a, A172, b, BT-474, c, BV-2, d, Huh7, e, MCF7, f, SH-SY5Y, g, SkBr3 and h, SK-OV-3 cells are shown in pairs, with the original phase-contrast image on the left and the overlaid annotations shown on the right in green. Images demonstrate morphological variety represented by the chosen cell types. Histograms show cell size distributions in µm2 for each cell type. On each histogram, the vertical color panes indicate the different cell size categories used for model evaluation and the percentages above each pane indicate how many in each cell type belong to each size category. The left-hand gray pane indicates small cells (defined as smaller than 320 µm2), the middle white pane indicates medium-sized cells (between 320 and 970 µm2) and the right-hand gray pane indicates large cells (larger than 970 µm2). Scale bar represents 150 µm and applies to all images.
Fig. 2 Illustrative examples of annotated phase-contrast microscopy images and histograms showing cell size distributions of all cell types in LIVECell. Example images for a, A172, b, BT-474, c, BV-2, d, Huh7, e, MCF7, f, SH-SY5Y, g, SkBr3 and h, SK-OV-3 cells are shown in pairs, with the original phase-contrast image on the left and the overlaid annotations shown on the right in green. Images demonstrate morphological variety represented by the chosen cell types. Histograms show cell size distributions in µm2 for each cell type. On each histogram, the vertical color panes indicate the different cell size categories used for model evaluation and the percentages above each pane indicate how many in each cell type belong to each size category. The left-hand gray pane indicates small cells (defined as smaller than 320 µm2), the middle white pane indicates medium-sized cells (between 320 and 970 µm2) and the right-hand gray pane indicates large cells (larger than 970 µm2). Scale bar represents 150 µm and applies to all images.CC BY · PubMed Central
Fig. 3 Performance evaluation of CNN models trained on LIVECell. a–f, Bar charts of cell segmentation performance, as reported by mask AP (%), for the LIVECell-wide train and evaluate task (a) and single cell-type train and evaluate task (c), cell detection performance, as reported by mask AFNR (%) for the LIVECell-wide train and evaluate task (b) and single cell-type train and evaluate task (d), as well as heatmaps for all possible transfers on the single cell-type model transferability test for the anchor-free (e) and anchor-based model (f) as reported by AP.
Fig. 3 Performance evaluation of CNN models trained on LIVECell. a–f, Bar charts of cell segmentation performance, as reported by mask AP (%), for the LIVECell-wide train and evaluate task (a) and single cell-type train and evaluate task (c), cell detection performance, as reported by mask AFNR (%) for the LIVECell-wide train and evaluate task (b) and single cell-type train and evaluate task (d), as well as heatmaps for all possible transfers on the single cell-type model transferability test for the anchor-free (e) and anchor-based model (f) as reported by AP.CC BY · PubMed Central
Fig. 4 Validation of anchor-free and anchor-based model using fluorescent nuclei count. a–h, Predicted model counts are compared to fluorescence nuclei counts on A172 and A549 cells. Time course graphs show per-image object counts across different cell seeding densities over time for fluorescent nuclei and the models for the anchor-free (a) and anchor-based (c) model for A172 cells and the anchor-free (e) and anchor-based (g) model on A549 cells. Correlation plots for each image show R2 > 0.99 with a gradient close to 1 when comparing nuclei count and label-free predictions of the anchor-free (b) and anchor-based (d) models for A172 cells and anchor-free (f) and anchor-based (h) models for A549 cells. The yellow markers highlight data removed from the correlation calculations. On all graphs, the dotted line represents the 95% cell confluence level. Data are shown as mean ± s.e.m. for n = 4 images (a,c) and n = 3 images (e,g).
Fig. 4 Validation of anchor-free and anchor-based model using fluorescent nuclei count. a–h, Predicted model counts are compared to fluorescence nuclei counts on A172 and A549 cells. Time course graphs show per-image object counts across different cell seeding densities over time for fluorescent nuclei and the models for the anchor-free (a) and anchor-based (c) model for A172 cells and the anchor-free (e) and anchor-based (g) model on A549 cells. Correlation plots for each image show R2 > 0.99 with a gradient close to 1 when comparing nuclei count and label-free predictions of the anchor-free (b) and anchor-based (d) models for A172 cells and anchor-free (f) and anchor-based (h) models for A549 cells. The yellow markers highlight data removed from the correlation calculations. On all graphs, the dotted line represents the 95% cell confluence level. Data are shown as mean ± s.e.m. for n = 4 images (a,c) and n = 3 images (e,g).CC BY · PubMed Central
Fig. 5 Impact of scale of dataset on segmentation performance. Each model was trained on subsets of the LIVECell training set, corresponding to 2, 4, 5, 25, 50 and 100% of total number of images. a,d, The resulting models were then evaluated by calculating segmentation AP on the complete LIVECell test (a) and AFNR (d). To further explore the effects of increasing the dataset size we broke down the metrics to each IoU level between 50 and 95% with a step size of 5%. b,c,e,f, The precision per IoU for the anchor-free (b) and anchor-based (c) models trained on different amounts of the dataset was calculated, as well as the FNR for the same anchor-free (e) and anchor-based models (f).
Fig. 5 Impact of scale of dataset on segmentation performance. Each model was trained on subsets of the LIVECell training set, corresponding to 2, 4, 5, 25, 50 and 100% of total number of images. a,d, The resulting models were then evaluated by calculating segmentation AP on the complete LIVECell test (a) and AFNR (d). To further explore the effects of increasing the dataset size we broke down the metrics to each IoU level between 50 and 95% with a step size of 5%. b,c,e,f, The precision per IoU for the anchor-free (b) and anchor-based (c) models trained on different amounts of the dataset was calculated, as well as the FNR for the same anchor-free (e) and anchor-based models (f).CC BY · PubMed Central

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  1. Levene, H. in Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling (eds Olkin, I. et al.) 279–292 (Stanford Univ. Press, 1961).

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Metadata

Authors
Edlund, C., Jackson, T. R., Khalid, N., Bevan, N., Dale, T., Dengel, A., Ahmed, S., Trygg, J., & Sjögren, R.
Affiliations
  • Sartorius (United Kingdom) (GB)
  • German Research Centre for Artificial Intelligence (DE)
  • Umeå University (SE)
Corresponding author
Sjögren, Rickard
ORCID
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2021-08-30
Volume
18
Issue
9
Pages
1038-1045
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/s41592-021-01249-6
PMC
PMC8440198
OpenAlex
W3196890347
Cited by
304
References
29
Citations by year
2026: 56 · 2025: 88 · 2024: 63 · 2023: 65 · 2022: 31 · 2021: 1
FWCI
18.09
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Methods (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DiVA at Umeå University (Umeå University) (submittedVersion, other-oa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
cell segmentation, label-free imaging, phase contrast microscopy, convolutional neural networks, segmentation benchmark, cell culture, high-throughput imaging, cell morphology
MeSH terms
Humans, Image Processing, Computer-Assisted, Microscopy, Models, Biological, Databases, Factual, Neural Networks, Computer, Cell Culture Techniques

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