High resolution annotated HiP-CT training dataset for paper - Multiscale Segmentation using Hierarchical Phase-contrast Tomography and Deep Learning

This dataset is associated with the publication: Multiscale Segmentation using Hierarchical Phase-contrast Tomography and Deep Learning. Codes available at https://github.com/UCL-MSM-Bio/2025-zhou-hipct-hierarchical-segmentation. The contents in the repository are: highres_training_16bit_cubes.zip: the original high-resolution HiP-CT cubes of size 512x512x512, 16 bits; highres_training_labels.zip: the manual annotations linked to the training cubes; Manual_annotated_high_resolution_data_spreadsheet.xlsx: details of each cube. If you use this dataset, please cite the paper and the dataset: @article{zhou2025multiscale, title={Multiscale Segmentation using Hierarchical Phase-contrast Tomography and Deep Learning}, author={Zhou, Yang and Aslani, Shahab and Javanmardi, Yousef and Brunet, Joseph and Standsby, David and Carroll, Saskia and Bellier, Alexandre and Ackermann, Maximilian and Tafforeau, Paul and Lee, Peter D and others}, journal={bioRxiv}, pages={2025--05}, year={2025}, publisher={Cold Spring Harbor Laboratory} }Zenodo. doi: 10.5281/zenodo.15397768. AcknowledgmentsThis project has been made possible in part by grant number 2022-316777 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. The authors would also like to express gratitude for ESRF beamtimes md1252 and md1290, Royal Academy of Engineering (CiET1819/10), and EPSRC grant JADE-2 [EP/T022205/1].