LungVis1.0: Active learning AI-powered 3D imaging ecosystem for spatial profiling of lung geometry and pulmonary nanoparticle delivery

The imaging dataset was obtained by light sheet fluorescence microscopy on tissue cleared murine lungs. It includes whole lung autofluorence image, particle fluorescence image, and artifical intelligence nnU-Net generated lung airway segments. The dataset provides 78 healthy murine lung strucutre and airway geometry for C57BL/6 mice and offers comprehensive delivery features including qualitative and quantitative analysis on the temporal and spatial inter- and intra-acinar deposition patterns and NP regional dosimetry for four commonly-used routes of pulmonary delivery,namely intranasal liquid aspiration, intratracheal liquid instillation, ventilator-assisted and nose-only aerosol inhalation.

Raw LSFM imaging data collection was carried out between 2017-2021,  the AI code and generated airway segmention were performed in 2021-2022, the whole datasets were then compiled in 2023. 

Please ensure to cite our paper for any reuse or reanalysis. Yang, L., Liu, Q., Kumar, P. et al. LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages. Nat Commun 15, 10138 (2024). https://doi.org/10.1038/s41467-024-54267-1

For any inquiries, please feel free to contact us at lin.yang@helmholtz-munich.de