Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging Article Swipe
Xiaoling Hu
,
Karthik Gopinath
,
Peirong Liu
,
Malte Hoffmann
,
Koen Van Leemput
,
Oula Puonti
,
Juan Eugenio Iglesias
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.09299
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.09299
Over recent years, deep learning based image registration has achieved impressive accuracy in many domains, including medical imaging and, specifically, human neuroimaging with magnetic resonance imaging (MRI). However, the uncertainty estimation associated with these methods has been largely limited to the application of generic techniques (e.g., Monte Carlo dropout) that do not exploit the peculiarities of the problem domain, particularly spatial modeling. Here, we propose a principled way to propagate uncertainties (epistemic or aleatoric) estimated at the level of spatial location by these methods, to the level of global transformation models, and further to downstream tasks. Specifically, we justify the choice of a Gaussian distribution for the local uncertainty modeling, and then propose a framework where uncertainties spread across hierarchical levels, depending on the choice of transformation model. Experiments on publicly available data sets show that Monte Carlo dropout correlates very poorly with the reference registration error, whereas our uncertainty estimates correlate much better. Crucially, the results also show that uncertainty-aware fitting of transformations improves the registration accuracy of brain MRI scans. Finally, we illustrate how sampling from the posterior distribution of the transformations can be used to propagate uncertainties to downstream neuroimaging tasks. Code is available at: https://github.com/HuXiaoling/Regre4Regis.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.09299
- https://arxiv.org/pdf/2410.09299
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403564540
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403564540Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2410.09299Digital Object Identifier
- Title
-
Hierarchical Uncertainty Estimation for Learning-based Registration in NeuroimagingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-11Full publication date if available
- Authors
-
Xiaoling Hu, Karthik Gopinath, Peirong Liu, Malte Hoffmann, Koen Van Leemput, Oula Puonti, Juan Eugenio IglesiasList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.09299Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2410.09299Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2410.09299Direct OA link when available
- Concepts
-
Neuroimaging, Estimation, Artificial intelligence, Computer science, Machine learning, Psychology, Neuroscience, Economics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403564540 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2410.09299 |
| ids.doi | https://doi.org/10.48550/arxiv.2410.09299 |
| ids.openalex | https://openalex.org/W4403564540 |
| fwci | |
| type | preprint |
| title | Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10052 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9567000269889832 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Medical Image Segmentation Techniques |
| topics[1].id | https://openalex.org/T10522 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9386000037193298 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Medical Imaging Techniques and Applications |
| topics[2].id | https://openalex.org/T10378 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.933899998664856 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2741 |
| topics[2].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[2].display_name | Advanced MRI Techniques and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C58693492 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8097295761108398 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q551875 |
| concepts[0].display_name | Neuroimaging |
| concepts[1].id | https://openalex.org/C96250715 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7245872020721436 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q965330 |
| concepts[1].display_name | Estimation |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6182093024253845 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.4877379834651947 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.43289902806282043 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C15744967 |
| concepts[5].level | 0 |
| concepts[5].score | 0.3312980532646179 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[5].display_name | Psychology |
| concepts[6].id | https://openalex.org/C169760540 |
| concepts[6].level | 1 |
| concepts[6].score | 0.12730422616004944 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[6].display_name | Neuroscience |
| concepts[7].id | https://openalex.org/C162324750 |
| concepts[7].level | 0 |
| concepts[7].score | 0.09775733947753906 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[7].display_name | Economics |
| concepts[8].id | https://openalex.org/C187736073 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[8].display_name | Management |
| keywords[0].id | https://openalex.org/keywords/neuroimaging |
| keywords[0].score | 0.8097295761108398 |
| keywords[0].display_name | Neuroimaging |
| keywords[1].id | https://openalex.org/keywords/estimation |
| keywords[1].score | 0.7245872020721436 |
| keywords[1].display_name | Estimation |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.6182093024253845 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.4877379834651947 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.43289902806282043 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/psychology |
| keywords[5].score | 0.3312980532646179 |
| keywords[5].display_name | Psychology |
| keywords[6].id | https://openalex.org/keywords/neuroscience |
| keywords[6].score | 0.12730422616004944 |
| keywords[6].display_name | Neuroscience |
| keywords[7].id | https://openalex.org/keywords/economics |
| keywords[7].score | 0.09775733947753906 |
| keywords[7].display_name | Economics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2410.09299 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2410.09299 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2410.09299 |
| locations[1].id | doi:10.48550/arxiv.2410.09299 |
| 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 | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2410.09299 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5022509823 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-3188-3005 |
| authorships[0].author.display_name | Xiaoling Hu |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hu, Xiaoling |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5054796268 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1949-0711 |
| authorships[1].author.display_name | Karthik Gopinath |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Gopinath, Karthik |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5043572938 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7645-009X |
| authorships[2].author.display_name | Peirong Liu |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Liu, Peirong |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101559837 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-5511-0739 |
| authorships[3].author.display_name | Malte Hoffmann |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Hoffmann, Malte |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5047520245 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6466-5309 |
| authorships[4].author.display_name | Koen Van Leemput |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Van Leemput, Koen |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5071613278 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-3186-244X |
| authorships[5].author.display_name | Oula Puonti |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Puonti, Oula |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5054888342 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-7569-173X |
| authorships[6].author.display_name | Juan Eugenio Iglesias |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Iglesias, Juan Eugenio |
| authorships[6].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/2410.09299 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10052 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9567000269889832 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Medical Image Segmentation Techniques |
| related_works | https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W3046775127, https://openalex.org/W3107602296, https://openalex.org/W4394896187, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W4364306694, https://openalex.org/W4312192474, https://openalex.org/W4283697347 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2410.09299 |
| 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/2410.09299 |
| 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/2410.09299 |
| primary_location.id | pmh:oai:arXiv.org:2410.09299 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2410.09299 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2410.09299 |
| publication_date | 2024-10-11 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 65, 102, 113 |
| abstract_inverted_index.at | 75 |
| abstract_inverted_index.be | 185 |
| abstract_inverted_index.by | 81 |
| abstract_inverted_index.do | 50 |
| abstract_inverted_index.in | 12 |
| abstract_inverted_index.is | 195 |
| abstract_inverted_index.of | 42, 55, 78, 87, 101, 125, 162, 168, 181 |
| abstract_inverted_index.on | 122, 129 |
| abstract_inverted_index.or | 72 |
| abstract_inverted_index.to | 39, 68, 84, 93, 187, 190 |
| abstract_inverted_index.we | 63, 97, 173 |
| abstract_inverted_index.MRI | 170 |
| abstract_inverted_index.and | 91, 110 |
| abstract_inverted_index.at: | 197 |
| abstract_inverted_index.can | 184 |
| abstract_inverted_index.for | 105 |
| abstract_inverted_index.has | 8, 35 |
| abstract_inverted_index.how | 175 |
| abstract_inverted_index.not | 51 |
| abstract_inverted_index.our | 148 |
| abstract_inverted_index.the | 28, 40, 53, 56, 76, 85, 99, 106, 123, 143, 155, 165, 178, 182 |
| abstract_inverted_index.way | 67 |
| abstract_inverted_index.Code | 194 |
| abstract_inverted_index.Over | 0 |
| abstract_inverted_index.also | 157 |
| abstract_inverted_index.and, | 18 |
| abstract_inverted_index.been | 36 |
| abstract_inverted_index.data | 132 |
| abstract_inverted_index.deep | 3 |
| abstract_inverted_index.from | 177 |
| abstract_inverted_index.many | 13 |
| abstract_inverted_index.much | 152 |
| abstract_inverted_index.sets | 133 |
| abstract_inverted_index.show | 134, 158 |
| abstract_inverted_index.that | 49, 135, 159 |
| abstract_inverted_index.then | 111 |
| abstract_inverted_index.used | 186 |
| abstract_inverted_index.very | 140 |
| abstract_inverted_index.with | 22, 32, 142 |
| abstract_inverted_index.Carlo | 47, 137 |
| abstract_inverted_index.Here, | 62 |
| abstract_inverted_index.Monte | 46, 136 |
| abstract_inverted_index.based | 5 |
| abstract_inverted_index.brain | 169 |
| abstract_inverted_index.human | 20 |
| abstract_inverted_index.image | 6 |
| abstract_inverted_index.level | 77, 86 |
| abstract_inverted_index.local | 107 |
| abstract_inverted_index.these | 33, 82 |
| abstract_inverted_index.where | 115 |
| abstract_inverted_index.(MRI). | 26 |
| abstract_inverted_index.(e.g., | 45 |
| abstract_inverted_index.across | 118 |
| abstract_inverted_index.choice | 100, 124 |
| abstract_inverted_index.error, | 146 |
| abstract_inverted_index.global | 88 |
| abstract_inverted_index.model. | 127 |
| abstract_inverted_index.poorly | 141 |
| abstract_inverted_index.recent | 1 |
| abstract_inverted_index.scans. | 171 |
| abstract_inverted_index.spread | 117 |
| abstract_inverted_index.tasks. | 95, 193 |
| abstract_inverted_index.years, | 2 |
| abstract_inverted_index.better. | 153 |
| abstract_inverted_index.domain, | 58 |
| abstract_inverted_index.dropout | 138 |
| abstract_inverted_index.exploit | 52 |
| abstract_inverted_index.fitting | 161 |
| abstract_inverted_index.further | 92 |
| abstract_inverted_index.generic | 43 |
| abstract_inverted_index.imaging | 17, 25 |
| abstract_inverted_index.justify | 98 |
| abstract_inverted_index.largely | 37 |
| abstract_inverted_index.levels, | 120 |
| abstract_inverted_index.limited | 38 |
| abstract_inverted_index.medical | 16 |
| abstract_inverted_index.methods | 34 |
| abstract_inverted_index.models, | 90 |
| abstract_inverted_index.problem | 57 |
| abstract_inverted_index.propose | 64, 112 |
| abstract_inverted_index.results | 156 |
| abstract_inverted_index.spatial | 60, 79 |
| abstract_inverted_index.whereas | 147 |
| abstract_inverted_index.Finally, | 172 |
| abstract_inverted_index.Gaussian | 103 |
| abstract_inverted_index.However, | 27 |
| abstract_inverted_index.accuracy | 11, 167 |
| abstract_inverted_index.achieved | 9 |
| abstract_inverted_index.domains, | 14 |
| abstract_inverted_index.dropout) | 48 |
| abstract_inverted_index.improves | 164 |
| abstract_inverted_index.learning | 4 |
| abstract_inverted_index.location | 80 |
| abstract_inverted_index.magnetic | 23 |
| abstract_inverted_index.methods, | 83 |
| abstract_inverted_index.publicly | 130 |
| abstract_inverted_index.sampling | 176 |
| abstract_inverted_index.available | 131, 196 |
| abstract_inverted_index.correlate | 151 |
| abstract_inverted_index.depending | 121 |
| abstract_inverted_index.estimated | 74 |
| abstract_inverted_index.estimates | 150 |
| abstract_inverted_index.framework | 114 |
| abstract_inverted_index.including | 15 |
| abstract_inverted_index.modeling, | 109 |
| abstract_inverted_index.modeling. | 61 |
| abstract_inverted_index.posterior | 179 |
| abstract_inverted_index.propagate | 69, 188 |
| abstract_inverted_index.reference | 144 |
| abstract_inverted_index.resonance | 24 |
| abstract_inverted_index.(epistemic | 71 |
| abstract_inverted_index.Crucially, | 154 |
| abstract_inverted_index.aleatoric) | 73 |
| abstract_inverted_index.associated | 31 |
| abstract_inverted_index.correlates | 139 |
| abstract_inverted_index.downstream | 94, 191 |
| abstract_inverted_index.estimation | 30 |
| abstract_inverted_index.illustrate | 174 |
| abstract_inverted_index.impressive | 10 |
| abstract_inverted_index.principled | 66 |
| abstract_inverted_index.techniques | 44 |
| abstract_inverted_index.Experiments | 128 |
| abstract_inverted_index.application | 41 |
| abstract_inverted_index.uncertainty | 29, 108, 149 |
| abstract_inverted_index.distribution | 104, 180 |
| abstract_inverted_index.hierarchical | 119 |
| abstract_inverted_index.neuroimaging | 21, 192 |
| abstract_inverted_index.particularly | 59 |
| abstract_inverted_index.registration | 7, 145, 166 |
| abstract_inverted_index.Specifically, | 96 |
| abstract_inverted_index.peculiarities | 54 |
| abstract_inverted_index.specifically, | 19 |
| abstract_inverted_index.uncertainties | 70, 116, 189 |
| abstract_inverted_index.transformation | 89, 126 |
| abstract_inverted_index.transformations | 163, 183 |
| abstract_inverted_index.uncertainty-aware | 160 |
| abstract_inverted_index.https://github.com/HuXiaoling/Regre4Regis. | 198 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |