Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
• Vertebrae are segmented with an iterative instance segmentation algorithm. • The method does not make assumptions about the number of visible vertebrae. • Detected vertebrae are anatomically labeled using a global probabilistic model. • A fully convolutional neural network performs both segmentati...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lessmann, Nikolas [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019 |
|---|
| Umfang: |
14 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Exergoeconomic analysis and multi-objective optimization of a semi-solar greenhouse with experimental validation - Mohammadi, Behzad ELSEVIER, 2019, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:53 ; year:2019 ; pages:142-155 ; extent:14 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.media.2019.02.005 |
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| 520 | |a • Vertebrae are segmented with an iterative instance segmentation algorithm. • The method does not make assumptions about the number of visible vertebrae. • Detected vertebrae are anatomically labeled using a global probabilistic model. • A fully convolutional neural network performs both segmentation and identification. • Vertebra segmentations and identifications are evaluated on five CT and MR datasets. | ||
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Iterative fully convolutional neural networks for automatic vertebra segmentation and identification |
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• Vertebrae are segmented with an iterative instance segmentation algorithm. • The method does not make assumptions about the number of visible vertebrae. • Detected vertebrae are anatomically labeled using a global probabilistic model. • A fully convolutional neural network performs both segmentation and identification. • Vertebra segmentations and identifications are evaluated on five CT and MR datasets. |
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• Vertebrae are segmented with an iterative instance segmentation algorithm. • The method does not make assumptions about the number of visible vertebrae. • Detected vertebrae are anatomically labeled using a global probabilistic model. • A fully convolutional neural network performs both segmentation and identification. • Vertebra segmentations and identifications are evaluated on five CT and MR datasets. |
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• Vertebrae are segmented with an iterative instance segmentation algorithm. • The method does not make assumptions about the number of visible vertebrae. • Detected vertebrae are anatomically labeled using a global probabilistic model. • A fully convolutional neural network performs both segmentation and identification. • Vertebra segmentations and identifications are evaluated on five CT and MR datasets. |
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Iterative fully convolutional neural networks for automatic vertebra segmentation and identification |
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