DOI: 10.5281/zenodo.14559104
title: Histology-informed microstructural diffusion simulations for MRI cancer characterisation (Histo-µSim): histology substrates

description: The Histo-μSim diffusion MRI technique: histology substrates

This data set contains the histological data used to build the Histo-μSim techniquefor the non-invasive characterisation of cancer properties through diffusion MRI.

The technique and the methods followed to acquire and process the histological datacontained in this data set can be found in our preprint:

"Histology-informed microstructural diffusion simulations for MRI cancer characterisation - the Histo-microSim framework"Athanasios Grigoriou, Carlos Macarro, Marco Palombo, Daniel Navarro-Garcia, Anna Voronova, Kinga Bernatowicz, Ignasi Barba, Alba Escriche, Emanuela Greco, Maria Abad, Sara Simonetti, Garazi Serna, Richard Mast, Xavier Merino, Nuria Roson, Manuel Escobar, Maria Vieito, Paolo Nuciforo, Rodrigo Toledo, Elena Garralda, Roser Sala-Llonch, Els Fieremans, Dmitry S. Novikov, Raquel Perez-Lopez and Francesco Grussu. medrxiv 2024, DOI: 10.1101/2024.07.15.24310280.https://doi.org/10.1101/2024.07.15.24310280Data descriptionThis data set contains 18 cellular environments reconstructed from hemaotxylin-eosin (HE) histology, referred to as  "substrates". For each substrate, we store the corresponding data in a folder (sub1, sub2, ...). The substrates match those reported in table 1 of our preprint (https://doi.org/10.1101/2024.07.15.24310280).

Unzip the compressed file HistouSim_substrates_v1.zip. This will extract the folder HistouSim_substrates_v1,containing version1 of the histological substrates used to build the Histo-microSim technique. 

Inside HistouSim_substrates_v1, you will find sub-folders sub1, sub2, ... subn, ... sub18. 

Each of these contains:- dims.txt: dimensions of the substrate (width and height, in μm). Note that the dimensions refer            to the WHOLE histological image, even if cells and other structures have been outlined            only in a sub-region of the image.  - info.txt: primary cancer type (if non-cancerous, it specifies that it corresponds to liver tissue)            and structures being segmented, with corresponding colourb (e.g., green: cells; etc)- subn.svg: the HE image with the outline structures, in different colours (cells, lumen, fat, vessels;            examples: sub1.svg within sub1, sub5.svg within sub5, etc)AcknowledgementsVHIO would like to acknowledge: the State Agency for Research (Agencia Estatal de Investigacion) for the financial support as a Center of Excellence Severo Ochoa (CEX2020-001024-S / AEI / 10.13039 / 501100011033), the Cellex Foundation for providing research facilities and equipment and the CERCA Programme from the Generalitat de Catalunya for their support on this research. This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project "PI21/01019" and co-funded by the European Union. Part of the data acquisitionhas been supported by PREdICT, sponsored by AstraZeneca. R.P.L is supported by the "la Caixa" Foundation CaixaResearch Advanced Oncology Research Program, the Prostate Cancer Foundation (18YOUN19), a CRIS Foundation Talent Award (TALENT19-05), the FERO Foundation through the XVIII Fero Fellowship for Oncological Research, the Instituto de Salud Carlos III-Investigacion en Salud (PI18/01395 and PI21/01019), the Asociacion Espanola Contra el Cancer (AECC) (PRYCO211023SERR) and the Generalitat de Catalunya Agency for Management of University and Research Grants of Catalonia (AGAUR) (2023PROD00178). The project that gave rise to these results received the support of a fellowship from "la Caixa" Foundation (ID 100010434). The fellowship code is "LCF/BQ/PR22/11920010" (funding F.G.). A.G. is supported by a Severo Ochoa PhD fellowship (PRE2022-102586). C.M. is funded by the Asociacion Espanola Contra el Cancer (AECC) (PRYCO211023SERR).