evoSegment: 4D image segmentation of microstructural evolution using joint histograms Article Swipe
Johan Hektor
,
Jonas Engqvist
,
Stephen A. Hall
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.tmater.2023.100023
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.tmater.2023.100023
A method for semantic segmentation of microstructure evolution from 4D imaging data is described and demonstrated. The method is based on a joint histogram describing the time history of the grayscale in each voxel of the images. After identifying and labeling clusters in the joint histogram, the labels are mapped back to the image. The results demonstrate accurate segmentation and characterization of sample evolution. The advantages of the proposed method include automatic segmentation of many time steps and the ability to track grayscale evolution over time and thereby discriminate similar evolution in different material phases. The method is demonstrated through application to 4D X-ray tomography datasets of temperature cycling in cement mortar and tensile testing of a cast iron sample. Water and air exchange in a pore inside the cement mortar is successfully segmented as a function of temperature. In the case of the deforming cast iron sample, several damage mechanisms are identified and segmented. The method is implemented in an open-source Python package called evoSegment.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.tmater.2023.100023
- OA Status
- hybrid
- Cited By
- 1
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389795477
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4389795477Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.tmater.2023.100023Digital Object Identifier
- Title
-
evoSegment: 4D image segmentation of microstructural evolution using joint histogramsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-15Full publication date if available
- Authors
-
Johan Hektor, Jonas Engqvist, Stephen A. HallList of authors in order
- Landing page
-
https://doi.org/10.1016/j.tmater.2023.100023Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.tmater.2023.100023Direct OA link when available
- Concepts
-
Artificial intelligence, Segmentation, Histogram, Grayscale, Computer science, Computer vision, Joint (building), Image segmentation, Voxel, Pattern recognition (psychology), Materials science, Pixel, Image (mathematics), Engineering, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4389795477 |
|---|---|
| doi | https://doi.org/10.1016/j.tmater.2023.100023 |
| ids.doi | https://doi.org/10.1016/j.tmater.2023.100023 |
| ids.openalex | https://openalex.org/W4389795477 |
| fwci | 0.18451112 |
| type | article |
| title | evoSegment: 4D image segmentation of microstructural evolution using joint histograms |
| awards[0].id | https://openalex.org/G918480985 |
| awards[0].funder_id | https://openalex.org/F4320321759 |
| awards[0].display_name | |
| awards[0].funder_award_id | 20190214 |
| awards[0].funder_display_name | Stiftelsen för Kunskaps- och Kompetensutveckling |
| awards[1].id | https://openalex.org/G6663521561 |
| awards[1].funder_id | https://openalex.org/F4320321030 |
| awards[1].display_name | |
| awards[1].funder_award_id | 2019-02556 |
| awards[1].funder_display_name | VINNOVA |
| awards[2].id | https://openalex.org/G2841286818 |
| awards[2].funder_id | https://openalex.org/F4320321761 |
| awards[2].display_name | |
| awards[2].funder_award_id | 20230630 |
| awards[2].funder_display_name | Crafoordska Stiftelsen |
| biblio.issue | |
| biblio.volume | 4 |
| biblio.last_page | 100023 |
| biblio.first_page | 100023 |
| topics[0].id | https://openalex.org/T12282 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9970999956130981 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2210 |
| topics[0].subfield.display_name | Mechanical Engineering |
| topics[0].display_name | Mineral Processing and Grinding |
| topics[1].id | https://openalex.org/T10161 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9955000281333923 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2211 |
| topics[1].subfield.display_name | Mechanics of Materials |
| topics[1].display_name | Rock Mechanics and Modeling |
| topics[2].id | https://openalex.org/T10892 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9926999807357788 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2212 |
| topics[2].subfield.display_name | Ocean Engineering |
| topics[2].display_name | Drilling and Well Engineering |
| funders[0].id | https://openalex.org/F4320321030 |
| funders[0].ror | https://ror.org/01kd5m353 |
| funders[0].display_name | VINNOVA |
| funders[1].id | https://openalex.org/F4320321759 |
| funders[1].ror | https://ror.org/02cbq7e25 |
| funders[1].display_name | Stiftelsen för Kunskaps- och Kompetensutveckling |
| funders[2].id | https://openalex.org/F4320321761 |
| funders[2].ror | https://ror.org/02hwwbr17 |
| funders[2].display_name | Crafoordska Stiftelsen |
| funders[3].id | https://openalex.org/F4320321806 |
| funders[3].ror | https://ror.org/012a77v79 |
| funders[3].display_name | Lunds Universitet |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.7074479460716248 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C89600930 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6486904621124268 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[1].display_name | Segmentation |
| concepts[2].id | https://openalex.org/C53533937 |
| concepts[2].level | 3 |
| concepts[2].score | 0.6436272859573364 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q185020 |
| concepts[2].display_name | Histogram |
| concepts[3].id | https://openalex.org/C78201319 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5841717720031738 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q685727 |
| concepts[3].display_name | Grayscale |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.5452819466590881 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C31972630 |
| concepts[5].level | 1 |
| concepts[5].score | 0.475740909576416 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[5].display_name | Computer vision |
| concepts[6].id | https://openalex.org/C18555067 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4559507966041565 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q8375051 |
| concepts[6].display_name | Joint (building) |
| concepts[7].id | https://openalex.org/C124504099 |
| concepts[7].level | 3 |
| concepts[7].score | 0.4493659734725952 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[7].display_name | Image segmentation |
| concepts[8].id | https://openalex.org/C54170458 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4484253227710724 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q663554 |
| concepts[8].display_name | Voxel |
| concepts[9].id | https://openalex.org/C153180895 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4381277561187744 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[9].display_name | Pattern recognition (psychology) |
| concepts[10].id | https://openalex.org/C192562407 |
| concepts[10].level | 0 |
| concepts[10].score | 0.4292994737625122 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[10].display_name | Materials science |
| concepts[11].id | https://openalex.org/C160633673 |
| concepts[11].level | 2 |
| concepts[11].score | 0.23956677317619324 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q355198 |
| concepts[11].display_name | Pixel |
| concepts[12].id | https://openalex.org/C115961682 |
| concepts[12].level | 2 |
| concepts[12].score | 0.18725991249084473 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[12].display_name | Image (mathematics) |
| concepts[13].id | https://openalex.org/C127413603 |
| concepts[13].level | 0 |
| concepts[13].score | 0.08534887433052063 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[13].display_name | Engineering |
| concepts[14].id | https://openalex.org/C66938386 |
| concepts[14].level | 1 |
| concepts[14].score | 0.08157274127006531 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[14].display_name | Structural engineering |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.7074479460716248 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/segmentation |
| keywords[1].score | 0.6486904621124268 |
| keywords[1].display_name | Segmentation |
| keywords[2].id | https://openalex.org/keywords/histogram |
| keywords[2].score | 0.6436272859573364 |
| keywords[2].display_name | Histogram |
| keywords[3].id | https://openalex.org/keywords/grayscale |
| keywords[3].score | 0.5841717720031738 |
| keywords[3].display_name | Grayscale |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.5452819466590881 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/computer-vision |
| keywords[5].score | 0.475740909576416 |
| keywords[5].display_name | Computer vision |
| keywords[6].id | https://openalex.org/keywords/joint |
| keywords[6].score | 0.4559507966041565 |
| keywords[6].display_name | Joint (building) |
| keywords[7].id | https://openalex.org/keywords/image-segmentation |
| keywords[7].score | 0.4493659734725952 |
| keywords[7].display_name | Image segmentation |
| keywords[8].id | https://openalex.org/keywords/voxel |
| keywords[8].score | 0.4484253227710724 |
| keywords[8].display_name | Voxel |
| keywords[9].id | https://openalex.org/keywords/pattern-recognition |
| keywords[9].score | 0.4381277561187744 |
| keywords[9].display_name | Pattern recognition (psychology) |
| keywords[10].id | https://openalex.org/keywords/materials-science |
| keywords[10].score | 0.4292994737625122 |
| keywords[10].display_name | Materials science |
| keywords[11].id | https://openalex.org/keywords/pixel |
| keywords[11].score | 0.23956677317619324 |
| keywords[11].display_name | Pixel |
| keywords[12].id | https://openalex.org/keywords/image |
| keywords[12].score | 0.18725991249084473 |
| keywords[12].display_name | Image (mathematics) |
| keywords[13].id | https://openalex.org/keywords/engineering |
| keywords[13].score | 0.08534887433052063 |
| keywords[13].display_name | Engineering |
| keywords[14].id | https://openalex.org/keywords/structural-engineering |
| keywords[14].score | 0.08157274127006531 |
| keywords[14].display_name | Structural engineering |
| language | en |
| locations[0].id | doi:10.1016/j.tmater.2023.100023 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4387285668 |
| locations[0].source.issn | 2949-673X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2949-673X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Tomography of Materials and Structures |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Tomography of Materials and Structures |
| locations[0].landing_page_url | https://doi.org/10.1016/j.tmater.2023.100023 |
| locations[1].id | pmh:oai:DiVA.org:mau-75302 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400013 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Publications (Konstfack University of Arts, Crafts, and Design) |
| locations[1].source.host_organization | https://openalex.org/I218177685 |
| locations[1].source.host_organization_name | University College of Arts Crafts and Design |
| locations[1].source.host_organization_lineage | https://openalex.org/I218177685 |
| locations[1].license | other-oa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | text |
| locations[1].license_id | https://openalex.org/licenses/other-oa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-75302 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5062280781 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-3454-2660 |
| authorships[0].author.display_name | Johan Hektor |
| authorships[0].countries | SE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I183111857 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Materials Science and Applied Mathematics, Malmö University, 205 06, Malmö, Sweden |
| authorships[0].institutions[0].id | https://openalex.org/I183111857 |
| authorships[0].institutions[0].ror | https://ror.org/05wp7an13 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I183111857 |
| authorships[0].institutions[0].country_code | SE |
| authorships[0].institutions[0].display_name | Malmö University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Johan Hektor |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Materials Science and Applied Mathematics, Malmö University, 205 06, Malmö, Sweden |
| authorships[1].author.id | https://openalex.org/A5031751219 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2630-8284 |
| authorships[1].author.display_name | Jonas Engqvist |
| authorships[1].countries | SE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I187531555 |
| authorships[1].affiliations[0].raw_affiliation_string | Division of Solid Mechanics, Lund University, Ole Römers Väg 1, 223 63, Lund, Sweden |
| authorships[1].institutions[0].id | https://openalex.org/I187531555 |
| authorships[1].institutions[0].ror | https://ror.org/012a77v79 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I187531555 |
| authorships[1].institutions[0].country_code | SE |
| authorships[1].institutions[0].display_name | Lund University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jonas Engqvist |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Division of Solid Mechanics, Lund University, Ole Römers Väg 1, 223 63, Lund, Sweden |
| authorships[2].author.id | https://openalex.org/A5087128702 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5232-4942 |
| authorships[2].author.display_name | Stephen A. Hall |
| authorships[2].countries | SE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I187531555 |
| authorships[2].affiliations[0].raw_affiliation_string | Division of Solid Mechanics, Lund University, Ole Römers Väg 1, 223 63, Lund, Sweden |
| authorships[2].institutions[0].id | https://openalex.org/I187531555 |
| authorships[2].institutions[0].ror | https://ror.org/012a77v79 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I187531555 |
| authorships[2].institutions[0].country_code | SE |
| authorships[2].institutions[0].display_name | Lund University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Stephen A. Hall |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Division of Solid Mechanics, Lund University, Ole Römers Väg 1, 223 63, Lund, Sweden |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.tmater.2023.100023 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | evoSegment: 4D image segmentation of microstructural evolution using joint histograms |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12282 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9970999956130981 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2210 |
| primary_topic.subfield.display_name | Mechanical Engineering |
| primary_topic.display_name | Mineral Processing and Grinding |
| related_works | https://openalex.org/W115686965, https://openalex.org/W2768918307, https://openalex.org/W3027020613, https://openalex.org/W2016533837, https://openalex.org/W2040020606, https://openalex.org/W2110031805, https://openalex.org/W3167885074, https://openalex.org/W2892386716, https://openalex.org/W1555939286, https://openalex.org/W1522196789 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.tmater.2023.100023 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4387285668 |
| best_oa_location.source.issn | 2949-673X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2949-673X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Tomography of Materials and Structures |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Tomography of Materials and Structures |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.tmater.2023.100023 |
| primary_location.id | doi:10.1016/j.tmater.2023.100023 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4387285668 |
| primary_location.source.issn | 2949-673X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2949-673X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Tomography of Materials and Structures |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Tomography of Materials and Structures |
| primary_location.landing_page_url | https://doi.org/10.1016/j.tmater.2023.100023 |
| publication_date | 2023-12-15 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W6759612919, https://openalex.org/W2972638288, https://openalex.org/W3210841342, https://openalex.org/W2739090799, https://openalex.org/W3030189511, https://openalex.org/W2991361150, https://openalex.org/W6782062203, https://openalex.org/W4384563144, https://openalex.org/W4362555720, https://openalex.org/W4220822106, https://openalex.org/W4294300627, https://openalex.org/W6750693228, https://openalex.org/W2133059825, https://openalex.org/W2145023731, https://openalex.org/W3170493236, https://openalex.org/W2989552938, https://openalex.org/W6772750526, https://openalex.org/W2943108026, https://openalex.org/W2039472483, https://openalex.org/W1998623546, https://openalex.org/W2031700541, https://openalex.org/W2000693406, https://openalex.org/W2089277641, https://openalex.org/W3092213525, https://openalex.org/W2093155661, https://openalex.org/W3208382710, https://openalex.org/W6792457987, https://openalex.org/W2343738910, https://openalex.org/W3161266507, https://openalex.org/W4280523310, https://openalex.org/W2636405537, https://openalex.org/W2658759501, https://openalex.org/W2147555557, https://openalex.org/W3041091508, https://openalex.org/W3003257820, https://openalex.org/W3017116419, https://openalex.org/W1570834090, https://openalex.org/W3132455321, https://openalex.org/W2917727060, https://openalex.org/W2801002582, https://openalex.org/W3047041352, https://openalex.org/W3141622563, https://openalex.org/W2155379062 |
| referenced_works_count | 43 |
| abstract_inverted_index.A | 0 |
| abstract_inverted_index.a | 21, 116, 125, 135 |
| abstract_inverted_index.4D | 9, 102 |
| abstract_inverted_index.In | 139 |
| abstract_inverted_index.an | 160 |
| abstract_inverted_index.as | 134 |
| abstract_inverted_index.in | 31, 42, 91, 109, 124, 159 |
| abstract_inverted_index.is | 12, 18, 97, 131, 157 |
| abstract_inverted_index.of | 5, 28, 34, 61, 66, 73, 106, 115, 137, 142 |
| abstract_inverted_index.on | 20 |
| abstract_inverted_index.to | 51, 80, 101 |
| abstract_inverted_index.The | 16, 54, 64, 95, 155 |
| abstract_inverted_index.air | 122 |
| abstract_inverted_index.and | 14, 39, 59, 77, 86, 112, 121, 153 |
| abstract_inverted_index.are | 48, 151 |
| abstract_inverted_index.for | 2 |
| abstract_inverted_index.the | 25, 29, 35, 43, 46, 52, 67, 78, 128, 140, 143 |
| abstract_inverted_index.back | 50 |
| abstract_inverted_index.case | 141 |
| abstract_inverted_index.cast | 117, 145 |
| abstract_inverted_index.data | 11 |
| abstract_inverted_index.each | 32 |
| abstract_inverted_index.from | 8 |
| abstract_inverted_index.iron | 118, 146 |
| abstract_inverted_index.many | 74 |
| abstract_inverted_index.over | 84 |
| abstract_inverted_index.pore | 126 |
| abstract_inverted_index.time | 26, 75, 85 |
| abstract_inverted_index.After | 37 |
| abstract_inverted_index.Water | 120 |
| abstract_inverted_index.X-ray | 103 |
| abstract_inverted_index.based | 19 |
| abstract_inverted_index.joint | 22, 44 |
| abstract_inverted_index.steps | 76 |
| abstract_inverted_index.track | 81 |
| abstract_inverted_index.voxel | 33 |
| abstract_inverted_index.Python | 162 |
| abstract_inverted_index.called | 164 |
| abstract_inverted_index.cement | 110, 129 |
| abstract_inverted_index.damage | 149 |
| abstract_inverted_index.image. | 53 |
| abstract_inverted_index.inside | 127 |
| abstract_inverted_index.labels | 47 |
| abstract_inverted_index.mapped | 49 |
| abstract_inverted_index.method | 1, 17, 69, 96, 156 |
| abstract_inverted_index.mortar | 111, 130 |
| abstract_inverted_index.sample | 62 |
| abstract_inverted_index.ability | 79 |
| abstract_inverted_index.cycling | 108 |
| abstract_inverted_index.history | 27 |
| abstract_inverted_index.images. | 36 |
| abstract_inverted_index.imaging | 10 |
| abstract_inverted_index.include | 70 |
| abstract_inverted_index.package | 163 |
| abstract_inverted_index.phases. | 94 |
| abstract_inverted_index.results | 55 |
| abstract_inverted_index.sample, | 147 |
| abstract_inverted_index.sample. | 119 |
| abstract_inverted_index.several | 148 |
| abstract_inverted_index.similar | 89 |
| abstract_inverted_index.tensile | 113 |
| abstract_inverted_index.testing | 114 |
| abstract_inverted_index.thereby | 87 |
| abstract_inverted_index.through | 99 |
| abstract_inverted_index.accurate | 57 |
| abstract_inverted_index.clusters | 41 |
| abstract_inverted_index.datasets | 105 |
| abstract_inverted_index.exchange | 123 |
| abstract_inverted_index.function | 136 |
| abstract_inverted_index.labeling | 40 |
| abstract_inverted_index.material | 93 |
| abstract_inverted_index.proposed | 68 |
| abstract_inverted_index.semantic | 3 |
| abstract_inverted_index.automatic | 71 |
| abstract_inverted_index.deforming | 144 |
| abstract_inverted_index.described | 13 |
| abstract_inverted_index.different | 92 |
| abstract_inverted_index.evolution | 7, 83, 90 |
| abstract_inverted_index.grayscale | 30, 82 |
| abstract_inverted_index.histogram | 23 |
| abstract_inverted_index.segmented | 133 |
| abstract_inverted_index.advantages | 65 |
| abstract_inverted_index.describing | 24 |
| abstract_inverted_index.evolution. | 63 |
| abstract_inverted_index.histogram, | 45 |
| abstract_inverted_index.identified | 152 |
| abstract_inverted_index.mechanisms | 150 |
| abstract_inverted_index.segmented. | 154 |
| abstract_inverted_index.tomography | 104 |
| abstract_inverted_index.application | 100 |
| abstract_inverted_index.demonstrate | 56 |
| abstract_inverted_index.evoSegment. | 165 |
| abstract_inverted_index.identifying | 38 |
| abstract_inverted_index.implemented | 158 |
| abstract_inverted_index.open-source | 161 |
| abstract_inverted_index.temperature | 107 |
| abstract_inverted_index.demonstrated | 98 |
| abstract_inverted_index.discriminate | 88 |
| abstract_inverted_index.segmentation | 4, 58, 72 |
| abstract_inverted_index.successfully | 132 |
| abstract_inverted_index.temperature. | 138 |
| abstract_inverted_index.demonstrated. | 15 |
| abstract_inverted_index.microstructure | 6 |
| abstract_inverted_index.characterization | 60 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5062280781 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I183111857 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.48154447 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |