{"url":"/dataset/full-spectral-autofluorescence-lifetime","name":"Full-Spectral Autofluorescence Lifetime Microscopic Images","full_name":null,"description_markdown":"- 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). \r\n- 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.\r\n- 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.\r\n- 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)","description_withheld":null,"homepage":"","introduced_date":"2022-02-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-learning-assisted-co-registration-of","title":"Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images","first_author":"Qiang Wang","url":null},"license":{"name":"CC BY 4.0","url":null},"modalities":[{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"name":"Hyperspectral images","url":"/datasets/modality/hyperspectral-images"}],"tasks":[{"name":"Medical Image Registration","url":"/task/medical-image-registration","datasets_with_task":"/datasets/task/medical-image-registration"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Full-Spectral Autofluorescence Lifetime Microscopic Images"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}