Quantitative analysis of brain herniation from non-contrast CT images using deep learning
• New predictors responsible for brain herniation is explored. • Error is computed in pixels, area and volume. • Volumetric relation of brain shift is explored.
Gespeichert in:
| Autor*in: |
Nag, Manas Kumar [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: 3203 – IDENTIFICATION OF DIFFERENTIATION ROOTS OF HEMATOPOIETIC STEM CELLS BY A PAIRED-DAUGHTER ASSAY COMBINED WITH MULTIPLE BARCODING - Tanaka, Yosuke ELSEVIER, 2022, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:349 ; year:2021 ; day:1 ; month:02 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.jneumeth.2020.109033 |
|---|
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ELV052705994 |
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Quantitative analysis of brain herniation from non-contrast CT images using deep learning |
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• New predictors responsible for brain herniation is explored. • Error is computed in pixels, area and volume. • Volumetric relation of brain shift is explored. |
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