413 individual HeLa cells were imaged to create this multimodal dataset for cytoskeletal analysis. The dataset comprises z-stacks across reflection interference contrast microscopy (RICM), brightfield, widefield fluorescence, total internal reflection fluorescence (TIRF), and confocal microscopy. It was curated by Carleton CTELab and last updated on 2026-05-25.
Use Cases
- Train segmentation models to identify cytoskeletal structures based on multimodal fluorescence images.
- Develop feature extraction algorithms for cell morphology quantification using brightfield and RICM images.
- Benchmark multimodal fusion techniques for correlating structural information from complementary microscopy modalities.
- Validate computer vision approaches for automated cell classification based on cytoskeletal organization patterns.
Strengths
- 413 individual cells provide a substantial sample for model training.
- Multimodal imaging includes five complementary microscopy techniques, enabling comparative analysis.
- The dataset is curated specifically for quantitative analysis with machine learning.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last updated 2026-05-25; freshness should be verified.
Provenance
- Source
- Carleton CTELab
- Collection Method
- Multimodal optical microscopy imaging of HeLa cells.
- Freshness
- 2026-05-25