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Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training

The dataset consists of 101 hyperspectral images of four 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 k...

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Detalles Bibliográficos
Autores principales: Puustinen, Sami, Hyttinen, Joni, Elomaa, Antti-Pekka, Vrzáková, Hana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482730/
https://www.ncbi.nlm.nih.gov/pubmed/37691737
http://dx.doi.org/10.1016/j.dib.2023.109526
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author Puustinen, Sami
Hyttinen, Joni
Elomaa, Antti-Pekka
Vrzáková, Hana
author_facet Puustinen, Sami
Hyttinen, Joni
Elomaa, Antti-Pekka
Vrzáková, Hana
author_sort Puustinen, Sami
collection PubMed
description The dataset consists of 101 hyperspectral images of four 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 is instrumental for advancing machine-learning algorithms and automated classification of anatomical structures, particularly the classification of superficial and deep vessels and transparent tissue layers.
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spelling pubmed-104827302023-09-08 Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training Puustinen, Sami Hyttinen, Joni Elomaa, Antti-Pekka Vrzáková, Hana Data Brief Data Article The dataset consists of 101 hyperspectral images of four 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 is instrumental for advancing machine-learning algorithms and automated classification of anatomical structures, particularly the classification of superficial and deep vessels and transparent tissue layers. Elsevier 2023-08-28 /pmc/articles/PMC10482730/ /pubmed/37691737 http://dx.doi.org/10.1016/j.dib.2023.109526 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Puustinen, Sami
Hyttinen, Joni
Elomaa, Antti-Pekka
Vrzáková, Hana
Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training
title Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training
title_full Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training
title_fullStr Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training
title_full_unstemmed Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training
title_short Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training
title_sort hyperspectral placenta dataset: hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482730/
https://www.ncbi.nlm.nih.gov/pubmed/37691737
http://dx.doi.org/10.1016/j.dib.2023.109526
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