dc.contributor.author | Puustinen, Sami,Kuopio University Hospital | |
dc.contributor.author | Hyttinen, Joni,University of Eastern Finland | |
dc.contributor.author | Elomaa, Antti-Pekka,Kuopio University Hospital | |
dc.contributor.author | Vrzáková, Hana,University of Eastern Finland | |
dc.date.accessioned | 2023-07-27T02:05:17Z | |
dc.date.available | 2023-07-27T02:05:17Z | |
dc.date.issued | 2023-07-26T09:51:14.004430+00:00 | |
dc.identifier.other | 10.5281/zenodo.8045940 | en |
dc.identifier.uri | https://erepo.uef.fi/handle/123456789/30090 | |
dc.description.abstract | The dataset consists of 101 hyperspectral images of four fresh human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset can be used to develop machine learning algorithms for the automated classification of biological structures, particularly the classification of superficial and deep vessels and transparent tissue layers. | |
dc.relation.uri | https://zenodo.org/record/8045940 | |
dc.subject | Medical hyperspectral imaging | |
dc.subject | Microsurgical training | |
dc.subject | Tissue classification | |
dc.subject | Hyperspectral dataset | |
dc.subject | Human placenta | |
dc.title | Hyperspectral Placenta Dataset: Hyperspectral Image Acquisition, Annotations, and Processing of Biological Tissues in Microsurgical Training | |
dc.relation.doi | 10.5281/zenodo.8045940 | |