A Framework for the Systematic Design of Segmentation Workflows

Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into custom...
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Gespeichert in:

Autor*in:	Iskakov, Almambet [verfasserIn] Kalidindi, Surya R. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Image segmentation Materials informatics Microstructure characterization Image processing Segmentation framework Digital microstructure

Übergeordnetes Werk:	Enthalten in: Integrating materials and manufacturing innovation - Berlin : SpringerOpen, 2012, 9(2020), 1 vom: 06. Jan., Seite 70-88
Übergeordnetes Werk:	volume:9 ; year:2020 ; number:1 ; day:06 ; month:01 ; pages:70-88

Links:	Volltext

DOI / URN:	10.1007/s40192-019-00166-z

Katalog-ID:	SPR039055760

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520			\|a Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures.
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allfields	10.1007/s40192-019-00166-z doi (DE-627)SPR039055760 (SPR)s40192-019-00166-z-e DE-627 ger DE-627 rakwb eng 620 ASE Iskakov, Almambet verfasserin aut A Framework for the Systematic Design of Segmentation Workflows 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures. Image segmentation (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Microstructure characterization (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Segmentation framework (dpeaa)DE-He213 Digital microstructure (dpeaa)DE-He213 Kalidindi, Surya R. verfasserin aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 9(2020), 1 vom: 06. Jan., Seite 70-88 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:9 year:2020 number:1 day:06 month:01 pages:70-88 https://dx.doi.org/10.1007/s40192-019-00166-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 1 06 01 70-88
spelling	10.1007/s40192-019-00166-z doi (DE-627)SPR039055760 (SPR)s40192-019-00166-z-e DE-627 ger DE-627 rakwb eng 620 ASE Iskakov, Almambet verfasserin aut A Framework for the Systematic Design of Segmentation Workflows 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures. Image segmentation (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Microstructure characterization (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Segmentation framework (dpeaa)DE-He213 Digital microstructure (dpeaa)DE-He213 Kalidindi, Surya R. verfasserin aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 9(2020), 1 vom: 06. Jan., Seite 70-88 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:9 year:2020 number:1 day:06 month:01 pages:70-88 https://dx.doi.org/10.1007/s40192-019-00166-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 1 06 01 70-88
allfields_unstemmed	10.1007/s40192-019-00166-z doi (DE-627)SPR039055760 (SPR)s40192-019-00166-z-e DE-627 ger DE-627 rakwb eng 620 ASE Iskakov, Almambet verfasserin aut A Framework for the Systematic Design of Segmentation Workflows 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures. Image segmentation (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Microstructure characterization (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Segmentation framework (dpeaa)DE-He213 Digital microstructure (dpeaa)DE-He213 Kalidindi, Surya R. verfasserin aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 9(2020), 1 vom: 06. Jan., Seite 70-88 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:9 year:2020 number:1 day:06 month:01 pages:70-88 https://dx.doi.org/10.1007/s40192-019-00166-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 1 06 01 70-88
allfieldsGer	10.1007/s40192-019-00166-z doi (DE-627)SPR039055760 (SPR)s40192-019-00166-z-e DE-627 ger DE-627 rakwb eng 620 ASE Iskakov, Almambet verfasserin aut A Framework for the Systematic Design of Segmentation Workflows 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures. Image segmentation (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Microstructure characterization (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Segmentation framework (dpeaa)DE-He213 Digital microstructure (dpeaa)DE-He213 Kalidindi, Surya R. verfasserin aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 9(2020), 1 vom: 06. Jan., Seite 70-88 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:9 year:2020 number:1 day:06 month:01 pages:70-88 https://dx.doi.org/10.1007/s40192-019-00166-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 1 06 01 70-88
allfieldsSound	10.1007/s40192-019-00166-z doi (DE-627)SPR039055760 (SPR)s40192-019-00166-z-e DE-627 ger DE-627 rakwb eng 620 ASE Iskakov, Almambet verfasserin aut A Framework for the Systematic Design of Segmentation Workflows 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures. Image segmentation (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Microstructure characterization (dpeaa)DE-He213 Image processing (dpeaa)DE-He213 Segmentation framework (dpeaa)DE-He213 Digital microstructure (dpeaa)DE-He213 Kalidindi, Surya R. verfasserin aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 9(2020), 1 vom: 06. Jan., Seite 70-88 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:9 year:2020 number:1 day:06 month:01 pages:70-88 https://dx.doi.org/10.1007/s40192-019-00166-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 9 2020 1 06 01 70-88
language	English
source	Enthalten in Integrating materials and manufacturing innovation 9(2020), 1 vom: 06. Jan., Seite 70-88 volume:9 year:2020 number:1 day:06 month:01 pages:70-88
sourceStr	Enthalten in Integrating materials and manufacturing innovation 9(2020), 1 vom: 06. Jan., Seite 70-88 volume:9 year:2020 number:1 day:06 month:01 pages:70-88
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image segmentation Materials informatics Microstructure characterization Image processing Segmentation framework Digital microstructure
dewey-raw	620
isfreeaccess_bool	false
container_title	Integrating materials and manufacturing innovation
authorswithroles_txt_mv	Iskakov, Almambet @@aut@@ Kalidindi, Surya R. @@aut@@
publishDateDaySort_date	2020-01-06T00:00:00Z
hierarchy_top_id	726496499
dewey-sort	3620
id	SPR039055760
language_de	englisch
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author	Iskakov, Almambet
spellingShingle	Iskakov, Almambet ddc 620 misc Image segmentation misc Materials informatics misc Microstructure characterization misc Image processing misc Segmentation framework misc Digital microstructure A Framework for the Systematic Design of Segmentation Workflows
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topic	ddc 620 misc Image segmentation misc Materials informatics misc Microstructure characterization misc Image processing misc Segmentation framework misc Digital microstructure
topic_unstemmed	ddc 620 misc Image segmentation misc Materials informatics misc Microstructure characterization misc Image processing misc Segmentation framework misc Digital microstructure
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title	A Framework for the Systematic Design of Segmentation Workflows
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title_full	A Framework for the Systematic Design of Segmentation Workflows
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title_sort	framework for the systematic design of segmentation workflows
title_auth	A Framework for the Systematic Design of Segmentation Workflows
abstract	Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures.
abstractGer	Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures.
abstract_unstemmed	Abstract Segmentation of microscopy images is an essential step in most experimental studies of process–structure–property relationships in advanced materials. Currently employed segmentation approaches require the user to identify and string together a sequence of algorithms (and codes) into customized workflows that need extensive tweaking and optimization (often accomplished through repeated trials) for producing the most reliable results for each set of images. Recent advances in materials characterization instruments have significantly increased the throughput and variety of microscopy images that could be generated in the efforts to document and understand the material internal structure. There is a critical need for a guiding framework for the systematic design of segmentation workflows that can eventually lead to fully automated segmentation workflows. In this work, we propose one such modular framework consisting of five sequential steps that is applicable to segmentation of a broad variety of microscopy images. Each step is designed to accomplish a specific subtask in the overall segmentation using available functions in popular software packages. Furthermore, the modular nature of the framework allows the user to explore alternate functions in each step, while systematically comparing their relative efficacies. We describe this new segmentation framework in this paper and demonstrate its value through case studies involving a variety of microstructures.
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title_short	A Framework for the Systematic Design of Segmentation Workflows
url	https://dx.doi.org/10.1007/s40192-019-00166-z
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author2	Kalidindi, Surya R.
author2Str	Kalidindi, Surya R.
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doi_str	10.1007/s40192-019-00166-z
up_date	2024-07-03T21:39:35.752Z
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