Example data for the paper "cubic: CUDA-Accelerated 3D Bioimage Computing"

Example image data accompanying the cubic Python library (CUDA-accelerated 3D bioimage computing). Used by the examples/notebooks/deconvolution_iterations_3d.ipynb notebook to demonstrate how PSNR, SSIM, FSC, and DCR can be used to track the progress of Richardson-Lucy 3D deconvolution and inform iteration-count selection. Files astr_vpa_hoechst.tif — A single 3D stack of Hoechst-stained astrocyte nuclei (16-bit). Acquired on a Yokogawa CQ1 confocal microscope. Voxel size: 0.1625 μm (xy) × 0.3 μm (z). astr_vpa_hoechst_psf_na095_cropped.tif — Theoretical 3D point spread function (PSF) modeled with the Richards & Wolf algorithm from the PSFGenerator ImageJ/Fiji plugin (NA = 0.95). Center-cropped to 30×210×210 (captures ≥ 99.5% of energy); the Richardson-Lucy deconvolution implementation zero-pads it to the image size internally. Citation If you use these data, please cite the cubic paper: A. A. Kalinin, A. E. Carpenter, S. Singh, and M. J. O’Meara, “Cubic: CUDA-Accelerated 3D Bioimage Computing,” in 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Honolulu, HI, USA, Oct. 2025, pp. 5831–5840, doi: 10.1109/ICCVW69036.2025.00608. BibTeX: @inproceedings{kalinin2025cubic, author = {Kalinin, Alexandr A. and Carpenter, Anne E. and Singh, Shantanu and O'Meara, Matthew J.}, title = {Cubic: {CUDA-Accelerated} {3D} {Bioimage} {Computing}}, booktitle = {2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)}, year = {2025}, pages = {5831--5840}, address = {Honolulu, HI, USA}, publisher = {IEEE}, doi = {10.1109/ICCVW69036.2025.00608} } Open access: CVF proceedings PDF. Preprint: arXiv:2510.14143.