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
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.] |
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Übergeordnetes Werk: |
volume:279 ; year:2017 ; day:1 ; month:03 ; pages:13-21 ; extent:9 |
Links: |
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DOI / URN: |
10.1016/j.jneumeth.2016.12.006 |
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Katalog-ID: |
ELV035713062 |
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10.1016/j.jneumeth.2016.12.006 doi GBVA2017004000011.pica (DE-627)ELV035713062 (ELSEVIER)S0165-0270(16)30292-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.86 bkl Ghani, Muhammad Usman verfasserin aut Dendritic spine classification using shape and appearance features based on two-photon microscopy 2017 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • 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. Dendritic spines Elsevier Shape analysis Elsevier Microscopy Elsevier Classification Elsevier Histogram of oriented gradients Elsevier Kernel density estimation Elsevier Disjunctive Normal Shape Model Elsevier Mesadi, Fitsum oth Kanık, Sümeyra Demir oth Argunşah, Ali Özgür oth Hobbiss, Anna Felicity oth Israely, Inbal oth Ünay, Devrim oth Taşdizen, Tolga oth Çetin, Müjdat oth Enthalten in Elsevier Science Tanaka, Yosuke ELSEVIER 3203 – IDENTIFICATION OF DIFFERENTIATION ROOTS OF HEMATOPOIETIC STEM CELLS BY A PAIRED-DAUGHTER ASSAY COMBINED WITH MULTIPLE BARCODING 2022 Amsterdam [u.a.] (DE-627)ELV008620644 volume:279 year:2017 day:1 month:03 pages:13-21 extent:9 https://doi.org/10.1016/j.jneumeth.2016.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.86 Hämatologie VZ AR 279 2017 1 0301 13-21 9 045F 610 |
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10.1016/j.jneumeth.2016.12.006 doi GBVA2017004000011.pica (DE-627)ELV035713062 (ELSEVIER)S0165-0270(16)30292-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.86 bkl Ghani, Muhammad Usman verfasserin aut Dendritic spine classification using shape and appearance features based on two-photon microscopy 2017 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • 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. Dendritic spines Elsevier Shape analysis Elsevier Microscopy Elsevier Classification Elsevier Histogram of oriented gradients Elsevier Kernel density estimation Elsevier Disjunctive Normal Shape Model Elsevier Mesadi, Fitsum oth Kanık, Sümeyra Demir oth Argunşah, Ali Özgür oth Hobbiss, Anna Felicity oth Israely, Inbal oth Ünay, Devrim oth Taşdizen, Tolga oth Çetin, Müjdat oth Enthalten in Elsevier Science Tanaka, Yosuke ELSEVIER 3203 – IDENTIFICATION OF DIFFERENTIATION ROOTS OF HEMATOPOIETIC STEM CELLS BY A PAIRED-DAUGHTER ASSAY COMBINED WITH MULTIPLE BARCODING 2022 Amsterdam [u.a.] (DE-627)ELV008620644 volume:279 year:2017 day:1 month:03 pages:13-21 extent:9 https://doi.org/10.1016/j.jneumeth.2016.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.86 Hämatologie VZ AR 279 2017 1 0301 13-21 9 045F 610 |
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10.1016/j.jneumeth.2016.12.006 doi GBVA2017004000011.pica (DE-627)ELV035713062 (ELSEVIER)S0165-0270(16)30292-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.86 bkl Ghani, Muhammad Usman verfasserin aut Dendritic spine classification using shape and appearance features based on two-photon microscopy 2017 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • 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. Dendritic spines Elsevier Shape analysis Elsevier Microscopy Elsevier Classification Elsevier Histogram of oriented gradients Elsevier Kernel density estimation Elsevier Disjunctive Normal Shape Model Elsevier Mesadi, Fitsum oth Kanık, Sümeyra Demir oth Argunşah, Ali Özgür oth Hobbiss, Anna Felicity oth Israely, Inbal oth Ünay, Devrim oth Taşdizen, Tolga oth Çetin, Müjdat oth Enthalten in Elsevier Science Tanaka, Yosuke ELSEVIER 3203 – IDENTIFICATION OF DIFFERENTIATION ROOTS OF HEMATOPOIETIC STEM CELLS BY A PAIRED-DAUGHTER ASSAY COMBINED WITH MULTIPLE BARCODING 2022 Amsterdam [u.a.] (DE-627)ELV008620644 volume:279 year:2017 day:1 month:03 pages:13-21 extent:9 https://doi.org/10.1016/j.jneumeth.2016.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.86 Hämatologie VZ AR 279 2017 1 0301 13-21 9 045F 610 |
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10.1016/j.jneumeth.2016.12.006 doi GBVA2017004000011.pica (DE-627)ELV035713062 (ELSEVIER)S0165-0270(16)30292-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 610 VZ 44.86 bkl Ghani, Muhammad Usman verfasserin aut Dendritic spine classification using shape and appearance features based on two-photon microscopy 2017 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • 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. Dendritic spines Elsevier Shape analysis Elsevier Microscopy Elsevier Classification Elsevier Histogram of oriented gradients Elsevier Kernel density estimation Elsevier Disjunctive Normal Shape Model Elsevier Mesadi, Fitsum oth Kanık, Sümeyra Demir oth Argunşah, Ali Özgür oth Hobbiss, Anna Felicity oth Israely, Inbal oth Ünay, Devrim oth Taşdizen, Tolga oth Çetin, Müjdat oth Enthalten in Elsevier Science Tanaka, Yosuke ELSEVIER 3203 – IDENTIFICATION OF DIFFERENTIATION ROOTS OF HEMATOPOIETIC STEM CELLS BY A PAIRED-DAUGHTER ASSAY COMBINED WITH MULTIPLE BARCODING 2022 Amsterdam [u.a.] (DE-627)ELV008620644 volume:279 year:2017 day:1 month:03 pages:13-21 extent:9 https://doi.org/10.1016/j.jneumeth.2016.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.86 Hämatologie VZ AR 279 2017 1 0301 13-21 9 045F 610 |
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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. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
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 |
remote_bool |
true |
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 |
ppnlink |
ELV008620644 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth oth oth oth oth oth |
doi_str |
10.1016/j.jneumeth.2016.12.006 |
up_date |
2024-07-06T18:16:52.485Z |
_version_ |
1803854610418368512 |
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score |
7.399457 |