High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models Article Swipe
Lívia Rodrigues
,
Martina Bocchetta
,
Oula Puonti
,
Douglas N. Greve
,
Ana Carolina Londe
,
Marcondes C. França
,
Simone Appenzeller
,
Letícia Rittner
,
Juan Eugenio Iglesias
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.19492
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.19492
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 HELM, Hypothalamic ex vivo Label Maps, 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.19492
- https://arxiv.org/pdf/2406.19492
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400222355
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400222355Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.19492Digital Object Identifier
- Title
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High-resolution segmentations of the hypothalamus and its subregions for training of segmentation modelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-27Full publication date if available
- Authors
-
Lívia Rodrigues, Martina Bocchetta, Oula Puonti, Douglas N. Greve, Ana Carolina Londe, Marcondes C. França, Simone Appenzeller, Letícia Rittner, Juan Eugenio IglesiasList of authors in order
- Landing page
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https://arxiv.org/abs/2406.19492Publisher landing page
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https://arxiv.org/pdf/2406.19492Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2406.19492Direct OA link when available
- Concepts
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Segmentation, Artificial intelligence, Training (meteorology), Computer science, High resolution, Resolution (logic), Training set, Hypothalamus, Pattern recognition (psychology), Computer vision, Neuroscience, Psychology, Geography, Remote sensing, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.scans, | 159 |
| abstract_inverted_index.topic, | 14 |
| abstract_inverted_index.brains. | 151 |
| abstract_inverted_index.cohorts | 39 |
| abstract_inverted_index.dataset | 93, 162 |
| abstract_inverted_index.develop | 116 |
| abstract_inverted_index.imaging | 7 |
| abstract_inverted_index.include | 168 |
| abstract_inverted_index.methods | 54, 118 |
| abstract_inverted_index.provide | 85, 154 |
| abstract_inverted_index.regions | 136 |
| abstract_inverted_index.support | 164 |
| abstract_inverted_index.However, | 48 |
| abstract_inverted_index.analyses | 22 |
| abstract_inverted_index.compared | 41 |
| abstract_inverted_index.composed | 94 |
| abstract_inverted_index.emerged, | 76 |
| abstract_inverted_index.magnetic | 5 |
| abstract_inverted_index.manually | 59, 174 |
| abstract_inverted_index.methods. | 68 |
| abstract_inverted_index.mirrored | 147 |
| abstract_inverted_index.obtained | 126 |
| abstract_inverted_index.projects | 166 |
| abstract_inverted_index.publicly | 100 |
| abstract_inverted_index.reducing | 77 |
| abstract_inverted_index.relevant | 12 |
| abstract_inverted_index.simulate | 149 |
| abstract_inverted_index.Automated | 29 |
| abstract_inverted_index.Recently, | 69 |
| abstract_inverted_index.analysis. | 28 |
| abstract_inverted_index.annotated | 60 |
| abstract_inverted_index.automated | 53, 138 |
| abstract_inverted_index.available | 101 |
| abstract_inverted_index.datasets, | 61 |
| abstract_inverted_index.different | 21 |
| abstract_inverted_index.resonance | 6 |
| abstract_inverted_index.synthetic | 73, 120 |
| abstract_inverted_index.volumetry | 25 |
| abstract_inverted_index.resolution | 103 |
| abstract_inverted_index.segmented. | 175 |
| abstract_inverted_index.structures | 3, 36, 170 |
| abstract_inverted_index.techniques | 71 |
| abstract_inverted_index.ultra-high | 102 |
| abstract_inverted_index.annotation. | 82 |
| abstract_inverted_index.combination | 129 |
| abstract_inverted_index.development | 50 |
| abstract_inverted_index.facilitates | 31 |
| abstract_inverted_index.Hypothalamic | 87 |
| abstract_inverted_index.Segmentation | 0 |
| abstract_inverted_index.hemispheres, | 110 |
| abstract_inverted_index.hypothalamic | 135 |
| abstract_inverted_index.neuroimaging | 13 |
| abstract_inverted_index.prerequisite | 19 |
| abstract_inverted_index.segmentation | 30, 117 |
| abstract_inverted_index.pre-processed | 156 |
| abstract_inverted_index.segmentation, | 44 |
| abstract_inverted_index.segmentations | 139 |
| abstract_inverted_index.time-consuming. | 47 |
| abstract_inverted_index.generalizability | 65 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |