Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views
• Minimal user interaction is needed for a good segmentation of the placenta. • Random forests with high level features improved the segmentation. • Higher accuracy than state-of-the-art interactive segmentation methods. • Co-segmentation of multiple volumes outperforms single sparse volume based me...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Guotai [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Umfang: |
11 |
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| 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:34 ; year:2016 ; pages:137-147 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.media.2016.04.009 |
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| 520 | |a • Minimal user interaction is needed for a good segmentation of the placenta. • Random forests with high level features improved the segmentation. • Higher accuracy than state-of-the-art interactive segmentation methods. • Co-segmentation of multiple volumes outperforms single sparse volume based method. | ||
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Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views |
| abstract |
• Minimal user interaction is needed for a good segmentation of the placenta. • Random forests with high level features improved the segmentation. • Higher accuracy than state-of-the-art interactive segmentation methods. • Co-segmentation of multiple volumes outperforms single sparse volume based method. |
| abstractGer |
• Minimal user interaction is needed for a good segmentation of the placenta. • Random forests with high level features improved the segmentation. • Higher accuracy than state-of-the-art interactive segmentation methods. • Co-segmentation of multiple volumes outperforms single sparse volume based method. |
| abstract_unstemmed |
• Minimal user interaction is needed for a good segmentation of the placenta. • Random forests with high level features improved the segmentation. • Higher accuracy than state-of-the-art interactive segmentation methods. • Co-segmentation of multiple volumes outperforms single sparse volume based method. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views |
| url |
https://doi.org/10.1016/j.media.2016.04.009 |
| remote_bool |
true |
| author2 |
Zuluaga, Maria A. Pratt, Rosalind Aertsen, Michael Doel, Tom Klusmann, Maria David, Anna L. Deprest, Jan Vercauteren, Tom Ourselin, Sébastien |
| author2Str |
Zuluaga, Maria A. Pratt, Rosalind Aertsen, Michael Doel, Tom Klusmann, Maria David, Anna L. Deprest, Jan Vercauteren, Tom Ourselin, Sébastien |
| ppnlink |
ELV003074609 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth oth oth oth oth |
| doi_str |
10.1016/j.media.2016.04.009 |
| up_date |
2024-07-06T22:36:29.640Z |
| _version_ |
1803870944248201216 |
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7.399748 |