Rodrigues, Livia;
Bocchetta, Martina;
Puonti, Oula;
Greve, Douglas;
Londe, Ana Carolina;
França, Marcondes;
Appenzeller, Simone;
... Iglesias, Juan Eugenio; + view all
(2024)
High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models.
Scientific Data
, 11
, Article 940. 10.1038/s41597-024-03775-2.
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Abstract
Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or shape analysis. Automated segmentation facilitates the study of brain structures in larger cohorts when compared with manual segmentation, which is time-consuming. However, the development of most automated methods relies on large and manually annotated datasets, which limits the generalizability of these methods. Recently, new techniques using synthetic images have emerged, reducing the need for manual annotation. Here we provide a dataset composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres, which can be used to develop segmentation methods using synthetic data. The label maps are obtained with a combination of manual labels for the hypothalamic regions and automated segmentations for the rest of the brain, and mirrored to simulate entire brains. We also provide the pre-processed ex vivo scans, as this dataset can support future projects to include other structures after these are manually segmented.
| Type: | Article |
|---|---|
| Title: | High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41597-024-03775-2 |
| Publisher version: | http://dx.doi.org/10.1038/s41597-024-03775-2 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10196492 |
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