Large Intestine 3D Shape Refinement Using Conditional Latent Point Diffusion Models Article Swipe
Kaouther Mouheb
,
Mobina Ghojogh Nejad
,
Lavsen Dahal
,
Ehsan Samei
,
W. Paul Segars
,
Joseph Y. Lo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-032-06774-6_8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-032-06774-6_8
Related Topics
Concepts
Artificial intelligence
Computer science
Signed distance function
Polygon mesh
Segmentation
Hausdorff distance
Point cloud
Computer vision
Surface reconstruction
Noise reduction
Deep learning
Imaging phantom
Pattern recognition (psychology)
Algorithm
Surface (topology)
Geometry
Mathematics
Computer graphics (images)
Physics
Optics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1007/978-3-032-06774-6_8
- OA Status
- green
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386841420
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386841420Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/978-3-032-06774-6_8Digital Object Identifier
- Title
-
Large Intestine 3D Shape Refinement Using Conditional Latent Point Diffusion ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-04Full publication date if available
- Authors
-
Kaouther Mouheb, Mobina Ghojogh Nejad, Lavsen Dahal, Ehsan Samei, W. Paul Segars, Joseph Y. LoList of authors in order
- Landing page
-
https://doi.org/10.1007/978-3-032-06774-6_8Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2309.08289Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, Signed distance function, Polygon mesh, Segmentation, Hausdorff distance, Point cloud, Computer vision, Surface reconstruction, Noise reduction, Deep learning, Imaging phantom, Pattern recognition (psychology), Algorithm, Surface (topology), Geometry, Mathematics, Computer graphics (images), Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4386841420 |
|---|---|
| doi | https://doi.org/10.1007/978-3-032-06774-6_8 |
| ids.doi | https://doi.org/10.48550/arxiv.2309.08289 |
| ids.openalex | https://openalex.org/W4386841420 |
| fwci | 0.0 |
| type | preprint |
| title | Large Intestine 3D Shape Refinement Using Conditional Latent Point Diffusion Models |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 116 |
| biblio.first_page | 103 |
| topics[0].id | https://openalex.org/T10719 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9948999881744385 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2206 |
| topics[0].subfield.display_name | Computational Mechanics |
| topics[0].display_name | 3D Shape Modeling and Analysis |
| topics[1].id | https://openalex.org/T10775 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9912999868392944 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Generative Adversarial Networks and Image Synthesis |
| topics[2].id | https://openalex.org/T10481 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9854999780654907 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1704 |
| topics[2].subfield.display_name | Computer Graphics and Computer-Aided Design |
| topics[2].display_name | Computer Graphics and Visualization Techniques |
| is_xpac | False |
| apc_list.value | 5000 |
| apc_list.currency | EUR |
| apc_list.value_usd | 5392 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6607805490493774 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6264330744743347 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C71169176 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6158015727996826 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7512907 |
| concepts[2].display_name | Signed distance function |
| concepts[3].id | https://openalex.org/C31487907 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6119335889816284 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1154597 |
| concepts[3].display_name | Polygon mesh |
| concepts[4].id | https://openalex.org/C89600930 |
| concepts[4].level | 2 |
| concepts[4].score | 0.610649585723877 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[4].display_name | Segmentation |
| concepts[5].id | https://openalex.org/C141898687 |
| concepts[5].level | 2 |
| concepts[5].score | 0.570078432559967 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1501997 |
| concepts[5].display_name | Hausdorff distance |
| concepts[6].id | https://openalex.org/C131979681 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5539332628250122 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1899648 |
| concepts[6].display_name | Point cloud |
| concepts[7].id | https://openalex.org/C31972630 |
| concepts[7].level | 1 |
| concepts[7].score | 0.5405082106590271 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[7].display_name | Computer vision |
| concepts[8].id | https://openalex.org/C20885615 |
| concepts[8].level | 3 |
| concepts[8].score | 0.5128360390663147 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q825595 |
| concepts[8].display_name | Surface reconstruction |
| concepts[9].id | https://openalex.org/C163294075 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4975793659687042 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q581861 |
| concepts[9].display_name | Noise reduction |
| concepts[10].id | https://openalex.org/C108583219 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4970450699329376 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[10].display_name | Deep learning |
| concepts[11].id | https://openalex.org/C104293457 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4864759147167206 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q28324852 |
| concepts[11].display_name | Imaging phantom |
| concepts[12].id | https://openalex.org/C153180895 |
| concepts[12].level | 2 |
| concepts[12].score | 0.36853688955307007 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[12].display_name | Pattern recognition (psychology) |
| concepts[13].id | https://openalex.org/C11413529 |
| concepts[13].level | 1 |
| concepts[13].score | 0.36447155475616455 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[13].display_name | Algorithm |
| concepts[14].id | https://openalex.org/C2776799497 |
| concepts[14].level | 2 |
| concepts[14].score | 0.3270593285560608 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q484298 |
| concepts[14].display_name | Surface (topology) |
| concepts[15].id | https://openalex.org/C2524010 |
| concepts[15].level | 1 |
| concepts[15].score | 0.20515930652618408 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[15].display_name | Geometry |
| concepts[16].id | https://openalex.org/C33923547 |
| concepts[16].level | 0 |
| concepts[16].score | 0.19196683168411255 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[16].display_name | Mathematics |
| concepts[17].id | https://openalex.org/C121684516 |
| concepts[17].level | 1 |
| concepts[17].score | 0.13394668698310852 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7600677 |
| concepts[17].display_name | Computer graphics (images) |
| concepts[18].id | https://openalex.org/C121332964 |
| concepts[18].level | 0 |
| concepts[18].score | 0.10708951950073242 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[18].display_name | Physics |
| concepts[19].id | https://openalex.org/C120665830 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[19].display_name | Optics |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.6607805490493774 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6264330744743347 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/signed-distance-function |
| keywords[2].score | 0.6158015727996826 |
| keywords[2].display_name | Signed distance function |
| keywords[3].id | https://openalex.org/keywords/polygon-mesh |
| keywords[3].score | 0.6119335889816284 |
| keywords[3].display_name | Polygon mesh |
| keywords[4].id | https://openalex.org/keywords/segmentation |
| keywords[4].score | 0.610649585723877 |
| keywords[4].display_name | Segmentation |
| keywords[5].id | https://openalex.org/keywords/hausdorff-distance |
| keywords[5].score | 0.570078432559967 |
| keywords[5].display_name | Hausdorff distance |
| keywords[6].id | https://openalex.org/keywords/point-cloud |
| keywords[6].score | 0.5539332628250122 |
| keywords[6].display_name | Point cloud |
| keywords[7].id | https://openalex.org/keywords/computer-vision |
| keywords[7].score | 0.5405082106590271 |
| keywords[7].display_name | Computer vision |
| keywords[8].id | https://openalex.org/keywords/surface-reconstruction |
| keywords[8].score | 0.5128360390663147 |
| keywords[8].display_name | Surface reconstruction |
| keywords[9].id | https://openalex.org/keywords/noise-reduction |
| keywords[9].score | 0.4975793659687042 |
| keywords[9].display_name | Noise reduction |
| keywords[10].id | https://openalex.org/keywords/deep-learning |
| keywords[10].score | 0.4970450699329376 |
| keywords[10].display_name | Deep learning |
| keywords[11].id | https://openalex.org/keywords/imaging-phantom |
| keywords[11].score | 0.4864759147167206 |
| keywords[11].display_name | Imaging phantom |
| keywords[12].id | https://openalex.org/keywords/pattern-recognition |
| keywords[12].score | 0.36853688955307007 |
| keywords[12].display_name | Pattern recognition (psychology) |
| keywords[13].id | https://openalex.org/keywords/algorithm |
| keywords[13].score | 0.36447155475616455 |
| keywords[13].display_name | Algorithm |
| keywords[14].id | https://openalex.org/keywords/surface |
| keywords[14].score | 0.3270593285560608 |
| keywords[14].display_name | Surface (topology) |
| keywords[15].id | https://openalex.org/keywords/geometry |
| keywords[15].score | 0.20515930652618408 |
| keywords[15].display_name | Geometry |
| keywords[16].id | https://openalex.org/keywords/mathematics |
| keywords[16].score | 0.19196683168411255 |
| keywords[16].display_name | Mathematics |
| keywords[17].id | https://openalex.org/keywords/computer-graphics |
| keywords[17].score | 0.13394668698310852 |
| keywords[17].display_name | Computer graphics (images) |
| keywords[18].id | https://openalex.org/keywords/physics |
| keywords[18].score | 0.10708951950073242 |
| keywords[18].display_name | Physics |
| language | en |
| locations[0].id | doi:10.1007/978-3-032-06774-6_8 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S106296714 |
| locations[0].source.issn | 0302-9743, 1611-3349 |
| locations[0].source.type | book series |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0302-9743 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Lecture notes in computer science |
| locations[0].source.host_organization | https://openalex.org/P4310319900 |
| locations[0].source.host_organization_name | Springer Science+Business Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | book-chapter |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Lecture Notes in Computer Science |
| locations[0].landing_page_url | https://doi.org/10.1007/978-3-032-06774-6_8 |
| locations[1].id | pmh:oai:arXiv.org:2309.08289 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | https://arxiv.org/pdf/2309.08289 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://arxiv.org/abs/2309.08289 |
| locations[2].id | doi:10.48550/arxiv.2309.08289 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.48550/arxiv.2309.08289 |
| indexed_in | arxiv, crossref, datacite |
| authorships[0].author.id | https://openalex.org/A5059739238 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8991-9405 |
| authorships[0].author.display_name | Kaouther Mouheb |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Mouheb, Kaouther |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5114083415 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Mobina Ghojogh Nejad |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Nejad, Mobina Ghojogh |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5070095990 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8991-759X |
| authorships[2].author.display_name | Lavsen Dahal |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dahal, Lavsen |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5021555712 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7451-3309 |
| authorships[3].author.display_name | Ehsan Samei |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Samei, Ehsan |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5046139669 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-3687-5733 |
| authorships[4].author.display_name | W. Paul Segars |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Segars, W. Paul |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5040192736 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-9540-5072 |
| authorships[5].author.display_name | Joseph Y. Lo |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Lo, Joseph Y. |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2309.08289 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-09-19T00:00:00 |
| display_name | Large Intestine 3D Shape Refinement Using Conditional Latent Point Diffusion Models |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10719 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9948999881744385 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2206 |
| primary_topic.subfield.display_name | Computational Mechanics |
| primary_topic.display_name | 3D Shape Modeling and Analysis |
| related_works | https://openalex.org/W2358149864, https://openalex.org/W3133812613, https://openalex.org/W4385921699, https://openalex.org/W4383558899, https://openalex.org/W4383558898, https://openalex.org/W2368534456, https://openalex.org/W2027037317, https://openalex.org/W2394211896, https://openalex.org/W2173552480, https://openalex.org/W2128668756 |
| cited_by_count | 0 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:arXiv.org:2309.08289 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2309.08289 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2309.08289 |
| primary_location.id | doi:10.1007/978-3-032-06774-6_8 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S106296714 |
| primary_location.source.issn | 0302-9743, 1611-3349 |
| primary_location.source.type | book series |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0302-9743 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Lecture notes in computer science |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | book-chapter |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Lecture Notes in Computer Science |
| primary_location.landing_page_url | https://doi.org/10.1007/978-3-032-06774-6_8 |
| publication_date | 2025-10-04 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3114961520, https://openalex.org/W2948523457, https://openalex.org/W2903814341, https://openalex.org/W4387226000, https://openalex.org/W2603502343, https://openalex.org/W3014974815, https://openalex.org/W4233857083, https://openalex.org/W3143455115, https://openalex.org/W1901129140, https://openalex.org/W1983281817, https://openalex.org/W2944579304, https://openalex.org/W2524140598, https://openalex.org/W4310461141, https://openalex.org/W2236995990, https://openalex.org/W4383218413, https://openalex.org/W4312360134, https://openalex.org/W2123732640 |
| referenced_works_count | 17 |
| abstract_inverted_index | |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.00032196 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |