BioTISR: Microtubules (3D)
Description
3D microtubules data of BioTISR dataset.
BioTISR is a biological image dataset for super-resolution microscopy, currently including 2D and 3D time-lapse image pairs of low-and-high resolution images of a variety of biology structures, aiming to provide a high-quality dataset of time-lapse biological SR images for the community to spark more developments of computational SR methods.
At present, 2D dataset includes five specimens (clathrin-coated pits, lysosomes, outer mitochondrial membrane, microtubules, and F-actin) acquired with the GI/TIRF-SIM mode and nonlinear SIM mode of our Multi-SIM system, and 3D data includes three specimens (outer mitochondrial membrane, microtubules, and F-actin) acquired with 3D-SIM mode of the Multi-SIM system. For each type of specimen and each imaging modality, we acquired the raw data from at least 50 distinct regions-of-interest (ROI). For each ROI, we acquired two (3D data) or three (2D data) groups of N-phase × M-orientation × T-timepoint raw images with a constant exposure time but increasing the excitation light intensity, where (N, M, T) are (3, 3, 20) for TIRF-SIM and GI-SIM, (5, 5, 10) for nonlinear SIM, and (3, 5, 10) for 3D-SIM.
The BioTISR dataset is related to the following paper:Chang Qiao, Shuran Liu, Yuwang Wang, Wencong Xu, et al. "Time-lapse Image Super-resolution Neural Network with Reliable Confidence Evaluation for Optical Microscopy." bioRxiv 2024.05.04.592503 (2024), which is an extension of our previously published BioSR dataset (https://www.nature.com/articles/s41592-020-01048-5).
Files
BioTISR_Microtubules_3D.zip
Files
(30.6 GB)
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Additional details
Related works
- Is described by
- Publication: 10.1101/2024.05.04.592503 (DOI)
- Is part of
- Dataset: 10.5281/zenodo.13924483 (DOI)
References
- Qiao, C., Li, D., Liu, Y. et al. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. Nat Biotechnol 41, 367–377 (2023).
- Qiao, C., Li, D., Guo, Y. et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat Methods 18, 194–202 (2021).