Full-Spectral Autofluorescence Lifetime Microscopic Images
Introduced by Wang et al. in Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images
- The dataset contains full-spectral autofluorescence lifetime microscopic images (FS-FLIM) acquired on unstained ex-vivo human lung tissue, where 100 4D hypercubes of 256x256 (spatial resolution) x 32 (time bins) x 512 (spectral channels from 500nm to 780nm). This dataset associates with our paper "Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images" (https://arxiv.org/abs/2202.07755) and "Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution" (https://doi.org/10.1038/s41467-021-26837-0).
- The FS-FLIM images provide transformative insights into human lung cancer with extra-dimensional information. This will enable visual and precise detection of early lung cancer. With the methodology in our co-registration paper, FS-FLIM images can be registered with H&E-stained histology images, allowing characterisation of tumour and surrounding cells at a celluar level with absolute autofluorescence lifetime.
- The dataset can be used for various purposes, including signal processing for optimal lifetime reconstruction, advanced image analysis for automatic feature extraction of lung cancer, and cellular-level characterisation of lung cancer with absolute label-free autofluorescence lifetime values.
- The dataset is available on the University of Edinburgh's DataShare (https://doi.org/10.7488/ds/3099 and https://doi.org/10.7488/ds/3421)
Papers
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