Dendritic spine classification using shape and appearance features based on two-photon microscopy
• A statistical machine learning-based spine classification approach is proposed. • Our classification framework enables to study the separability of spine shape classes. • Proposed approach outperforms state-of-the-art morphological feature based methods. • A fully annotated dataset of 2PLSM images...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ghani, Muhammad Usman [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Umfang: |
9 |
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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.] |
|---|---|
| Übergeordnetes Werk: |
volume:279 ; year:2017 ; day:1 ; month:03 ; pages:13-21 ; extent:9 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jneumeth.2016.12.006 |
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ELV035713062 |
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| 520 | |a • A statistical machine learning-based spine classification approach is proposed. • Our classification framework enables to study the separability of spine shape classes. • Proposed approach outperforms state-of-the-art morphological feature based methods. • A fully annotated dataset of 2PLSM images of three types of spines will be released. | ||
| 650 | 7 | |a Dendritic spines |2 Elsevier | |
| 650 | 7 | |a Shape analysis |2 Elsevier | |
| 650 | 7 | |a Microscopy |2 Elsevier | |
| 650 | 7 | |a Classification |2 Elsevier | |
| 650 | 7 | |a Histogram of oriented gradients |2 Elsevier | |
| 650 | 7 | |a Kernel density estimation |2 Elsevier | |
| 650 | 7 | |a Disjunctive Normal Shape Model |2 Elsevier | |
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| 700 | 1 | |a Hobbiss, Anna Felicity |4 oth | |
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Elektronische Aufsätze |
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Ghani, Muhammad Usman |
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10.1016/j.jneumeth.2016.12.006 |
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610 |
| title_sort |
dendritic spine classification using shape and appearance features based on two-photon microscopy |
| title_auth |
Dendritic spine classification using shape and appearance features based on two-photon microscopy |
| abstract |
• A statistical machine learning-based spine classification approach is proposed. • Our classification framework enables to study the separability of spine shape classes. • Proposed approach outperforms state-of-the-art morphological feature based methods. • A fully annotated dataset of 2PLSM images of three types of spines will be released. |
| abstractGer |
• A statistical machine learning-based spine classification approach is proposed. • Our classification framework enables to study the separability of spine shape classes. • Proposed approach outperforms state-of-the-art morphological feature based methods. • A fully annotated dataset of 2PLSM images of three types of spines will be released. |
| abstract_unstemmed |
• A statistical machine learning-based spine classification approach is proposed. • Our classification framework enables to study the separability of spine shape classes. • Proposed approach outperforms state-of-the-art morphological feature based methods. • A fully annotated dataset of 2PLSM images of three types of spines will be released. |
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| title_short |
Dendritic spine classification using shape and appearance features based on two-photon microscopy |
| url |
https://doi.org/10.1016/j.jneumeth.2016.12.006 |
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| author2 |
Mesadi, Fitsum Kanık, Sümeyra Demir Argunşah, Ali Özgür Hobbiss, Anna Felicity Israely, Inbal Ünay, Devrim Taşdizen, Tolga Çetin, Müjdat |
| author2Str |
Mesadi, Fitsum Kanık, Sümeyra Demir Argunşah, Ali Özgür Hobbiss, Anna Felicity Israely, Inbal Ünay, Devrim Taşdizen, Tolga Çetin, Müjdat |
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| doi_str |
10.1016/j.jneumeth.2016.12.006 |
| up_date |
2024-07-06T18:16:52.485Z |
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