3D cell nuclei segmentation based on gradient flow tracking

<p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic i...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Nie Jingxin [verfasserIn] Tarokh Ashley [verfasserIn] Liu Tianming [verfasserIn] Li Gang [verfasserIn] Guo Lei [verfasserIn] Mara Andrew [verfasserIn] Holley Scott [verfasserIn] Wong Stephen TC [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2007

Übergeordnetes Werk:	In: BMC Cell Biology - BMC, 2003, 8(2007), 1, p 40
Übergeordnetes Werk:	volume:8 ; year:2007 ; number:1, p 40

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1471-2121-8-40

Katalog-ID:	DOAJ06716238X

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allfields	10.1186/1471-2121-8-40 doi (DE-627)DOAJ06716238X (DE-599)DOAJ50eef54cec8e43e8adb0376267c62de6 DE-627 ger DE-627 rakwb eng QH573-671 Nie Jingxin verfasserin aut 3D cell nuclei segmentation based on gradient flow tracking 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p< <p<Conclusion</p< <p<The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p< Cytology Tarokh Ashley verfasserin aut Liu Tianming verfasserin aut Li Gang verfasserin aut Guo Lei verfasserin aut Mara Andrew verfasserin aut Holley Scott verfasserin aut Wong Stephen TC verfasserin aut In BMC Cell Biology BMC, 2003 8(2007), 1, p 40 (DE-627)326644830 (DE-600)2041486-9 14712121 nnns volume:8 year:2007 number:1, p 40 https://doi.org/10.1186/1471-2121-8-40 kostenfrei https://doaj.org/article/50eef54cec8e43e8adb0376267c62de6 kostenfrei http://www.biomedcentral.com/1471-2121/8/40 kostenfrei https://doaj.org/toc/1471-2121 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 40
spelling	10.1186/1471-2121-8-40 doi (DE-627)DOAJ06716238X (DE-599)DOAJ50eef54cec8e43e8adb0376267c62de6 DE-627 ger DE-627 rakwb eng QH573-671 Nie Jingxin verfasserin aut 3D cell nuclei segmentation based on gradient flow tracking 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p< <p<Conclusion</p< <p<The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p< Cytology Tarokh Ashley verfasserin aut Liu Tianming verfasserin aut Li Gang verfasserin aut Guo Lei verfasserin aut Mara Andrew verfasserin aut Holley Scott verfasserin aut Wong Stephen TC verfasserin aut In BMC Cell Biology BMC, 2003 8(2007), 1, p 40 (DE-627)326644830 (DE-600)2041486-9 14712121 nnns volume:8 year:2007 number:1, p 40 https://doi.org/10.1186/1471-2121-8-40 kostenfrei https://doaj.org/article/50eef54cec8e43e8adb0376267c62de6 kostenfrei http://www.biomedcentral.com/1471-2121/8/40 kostenfrei https://doaj.org/toc/1471-2121 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 40
allfields_unstemmed	10.1186/1471-2121-8-40 doi (DE-627)DOAJ06716238X (DE-599)DOAJ50eef54cec8e43e8adb0376267c62de6 DE-627 ger DE-627 rakwb eng QH573-671 Nie Jingxin verfasserin aut 3D cell nuclei segmentation based on gradient flow tracking 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p< <p<Conclusion</p< <p<The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p< Cytology Tarokh Ashley verfasserin aut Liu Tianming verfasserin aut Li Gang verfasserin aut Guo Lei verfasserin aut Mara Andrew verfasserin aut Holley Scott verfasserin aut Wong Stephen TC verfasserin aut In BMC Cell Biology BMC, 2003 8(2007), 1, p 40 (DE-627)326644830 (DE-600)2041486-9 14712121 nnns volume:8 year:2007 number:1, p 40 https://doi.org/10.1186/1471-2121-8-40 kostenfrei https://doaj.org/article/50eef54cec8e43e8adb0376267c62de6 kostenfrei http://www.biomedcentral.com/1471-2121/8/40 kostenfrei https://doaj.org/toc/1471-2121 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 40
allfieldsGer	10.1186/1471-2121-8-40 doi (DE-627)DOAJ06716238X (DE-599)DOAJ50eef54cec8e43e8adb0376267c62de6 DE-627 ger DE-627 rakwb eng QH573-671 Nie Jingxin verfasserin aut 3D cell nuclei segmentation based on gradient flow tracking 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p< <p<Conclusion</p< <p<The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p< Cytology Tarokh Ashley verfasserin aut Liu Tianming verfasserin aut Li Gang verfasserin aut Guo Lei verfasserin aut Mara Andrew verfasserin aut Holley Scott verfasserin aut Wong Stephen TC verfasserin aut In BMC Cell Biology BMC, 2003 8(2007), 1, p 40 (DE-627)326644830 (DE-600)2041486-9 14712121 nnns volume:8 year:2007 number:1, p 40 https://doi.org/10.1186/1471-2121-8-40 kostenfrei https://doaj.org/article/50eef54cec8e43e8adb0376267c62de6 kostenfrei http://www.biomedcentral.com/1471-2121/8/40 kostenfrei https://doaj.org/toc/1471-2121 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 40
allfieldsSound	10.1186/1471-2121-8-40 doi (DE-627)DOAJ06716238X (DE-599)DOAJ50eef54cec8e43e8adb0376267c62de6 DE-627 ger DE-627 rakwb eng QH573-671 Nie Jingxin verfasserin aut 3D cell nuclei segmentation based on gradient flow tracking 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p< <p<Conclusion</p< <p<The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p< Cytology Tarokh Ashley verfasserin aut Liu Tianming verfasserin aut Li Gang verfasserin aut Guo Lei verfasserin aut Mara Andrew verfasserin aut Holley Scott verfasserin aut Wong Stephen TC verfasserin aut In BMC Cell Biology BMC, 2003 8(2007), 1, p 40 (DE-627)326644830 (DE-600)2041486-9 14712121 nnns volume:8 year:2007 number:1, p 40 https://doi.org/10.1186/1471-2121-8-40 kostenfrei https://doaj.org/article/50eef54cec8e43e8adb0376267c62de6 kostenfrei http://www.biomedcentral.com/1471-2121/8/40 kostenfrei https://doaj.org/toc/1471-2121 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 40
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authorswithroles_txt_mv	Nie Jingxin @@aut@@ Tarokh Ashley @@aut@@ Liu Tianming @@aut@@ Li Gang @@aut@@ Guo Lei @@aut@@ Mara Andrew @@aut@@ Holley Scott @@aut@@ Wong Stephen TC @@aut@@
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topic_title	QH573-671 3D cell nuclei segmentation based on gradient flow tracking
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title	3D cell nuclei segmentation based on gradient flow tracking
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title_full	3D cell nuclei segmentation based on gradient flow tracking
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title_sort	3d cell nuclei segmentation based on gradient flow tracking
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title_auth	3D cell nuclei segmentation based on gradient flow tracking
abstract	<p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p< <p<Conclusion</p< <p<The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p< <p<Conclusion</p< <p<The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.</p< <p<Conclusion</p< <p<The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.</p<
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title_short	3D cell nuclei segmentation based on gradient flow tracking
url	https://doi.org/10.1186/1471-2121-8-40 https://doaj.org/article/50eef54cec8e43e8adb0376267c62de6 http://www.biomedcentral.com/1471-2121/8/40 https://doaj.org/toc/1471-2121
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We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.</p< <p<Results</p< <p<Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. 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3D cell nuclei segmentation based on gradient flow tracking

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