Replication Data for: EntropyMasker

DOI

Background EntropyMasker is a fully automated approach for separating foreground (tissue) and background in bright-field microscopic whole-slide images of (immuno)histologically stained samples. This method is unaffected by changes in scanning or image processing conditions, by using a measure of local entropy and generating corresponding binary tissue masks.

ExpressScan The ExpressScan is an ongoing, unfunded project to scan pathological slides of atherosclerotic plaques and aneurysm tissues at high-resolution using pathology scanners into whole-slide images (WSI). Here we describe these histological WSI data used for the EntropyMasker project from the Athero-Express (AE) Biobank Studies.

Athero-Express Biobank Study The AE started in 2002 and now includes over 3,500 patients who underwent surgery to remove atherosclerotic plaques (endarterectomy) from one (or more) of their major arteries (majority carotids and femorals); this is further described here. The study design and staining protocols are described by Verhoeven et al..

A link to the public GitHub repository for EntropyMasker can be found here: https://github.com/CirculatoryHealth/EntropyMasker.

Important notice on availability of data The amount of data is huge on average 1Gb size per WSI. There are also restrictions on use by commercial parties, and on sharing openly based on (inter)national laws and regulations and the written informed consent. Therefore these data (and additional clinical data) are only available upon discussion and signing a Data Sharing Agreement (see Terms of Access) and within a specially designed UMC Utrecht provided environment.

Identifier
DOI https://doi.org/10.34894/GN4YOS
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/GN4YOS
Provenance
Creator Yipei Song; Craig Glastonbury ORCID logo; Sander W. van der Laan ORCID logo; Clint L. Miller ORCID logo; Francesco Cisternino
Publisher DataverseNL
Contributor dLAB Datamanagement; Sander W. van der Laan
Publication Year 2022
Rights info:eu-repo/semantics/restrictedAccess
OpenAccess false
Contact dLAB Datamanagement (UMC Utrecht); Sander W. van der Laan (UMC Utrecht)
Representation
Resource Type Dataset
Format application/pdf; application/msword; text/plain
Size 61663; 64158; 34816; 3611; 3058
Version 2.0
Discipline Life Sciences; Medicine