Regional perception and multi-scale feature fusion network for cardiac segmentation Article Swipe
Chenggang Lu
,
Jinli Yuan
,
Kewen Xia
,
Zhitao Guo
,
Muxuan Chen
,
Hengyong Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acc71f
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acc71f
Objective. Cardiovascular disease (CVD) is a group of diseases affecting cardiac and blood vessels, and short-axis cardiac magnetic resonance (CMR) images are considered the gold standard for the diagnosis and assessment of CVD. In CMR images, accurate segmentation of cardiac structures (e.g. left ventricle) assists in the parametric quantification of cardiac function. However, the dynamic beating of the heart renders the location of the heart with respect to other tissues difficult to resolve, and the myocardium and its surrounding tissues are similar in grayscale. This makes it challenging to accurately segment the cardiac images. Our goal is to develop a more accurate CMR image segmentation approach. Approach. In this study, we propose a regional perception and multi-scale feature fusion network (RMFNet) for CMR image segmentation. We design two regional perception modules, a window selection transformer (WST) module and a grid extraction transformer (GET) module. The WST module introduces a window selection block to adaptively select the window of interest to perceive information, and a windowed transformer block to enhance global information extraction within each feature window. The WST module enhances the network performance by improving the window of interest. The GET module grids the feature maps to decrease the redundant information in the feature maps and enhances the extraction of latent feature information of the network. The RMFNet further introduces a novel multi-scale feature extraction module to improve the ability to retain detailed information. Main results. The RMFNet is validated with experiments on three cardiac data sets. The results show that the RMFNet outperforms other advanced methods in overall performance. The RMFNet is further validated for generalizability on a multi-organ data set. The results also show that the RMFNet surpasses other comparison methods. Significance. Accurate medical image segmentation can reduce the stress of radiologists and play an important role in image-guided clinical procedures.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/acc71f
- OA Status
- hybrid
- Cited By
- 7
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4360603443
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4360603443Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/acc71fDigital Object Identifier
- Title
-
Regional perception and multi-scale feature fusion network for cardiac segmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-23Full publication date if available
- Authors
-
Chenggang Lu, Jinli Yuan, Kewen Xia, Zhitao Guo, Muxuan Chen, Hengyong YuList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/acc71fPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1361-6560/acc71fDirect OA link when available
- Concepts
-
Feature (linguistics), Segmentation, Scale (ratio), Artificial intelligence, Perception, Fusion, Computer science, Pattern recognition (psychology), Computer vision, Cartography, Geography, Psychology, Neuroscience, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4360603443 |
|---|---|
| doi | https://doi.org/10.1088/1361-6560/acc71f |
| ids.doi | https://doi.org/10.1088/1361-6560/acc71f |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/36958057 |
| ids.openalex | https://openalex.org/W4360603443 |
| fwci | 1.27377838 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D006321 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Heart |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D006352 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Heart Ventricles |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D009206 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Myocardium |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D002318 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Cardiovascular Diseases |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D010465 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Perception |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D007091 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Image Processing, Computer-Assisted |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D006801 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Humans |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D006321 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Heart |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D006352 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Heart Ventricles |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D009206 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Myocardium |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D002318 |
| mesh[11].is_major_topic | True |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Cardiovascular Diseases |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D010465 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Perception |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D007091 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Image Processing, Computer-Assisted |
| type | article |
| title | Regional perception and multi-scale feature fusion network for cardiac segmentation |
| biblio.issue | 10 |
| biblio.volume | 68 |
| biblio.last_page | 105003 |
| biblio.first_page | 105003 |
| 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 | 0.9948999881744385 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/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/T10688 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9822999835014343 |
| 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 | Image and Signal Denoising Methods |
| topics[2].id | https://openalex.org/T12386 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9783999919891357 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2204 |
| topics[2].subfield.display_name | Biomedical Engineering |
| topics[2].display_name | Advanced X-ray and CT Imaging |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2776401178 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7064210772514343 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[0].display_name | Feature (linguistics) |
| concepts[1].id | https://openalex.org/C89600930 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6982797980308533 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[1].display_name | Segmentation |
| concepts[2].id | https://openalex.org/C2778755073 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6776217222213745 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q10858537 |
| concepts[2].display_name | Scale (ratio) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6167075037956238 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C26760741 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6132376790046692 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q160402 |
| concepts[4].display_name | Perception |
| concepts[5].id | https://openalex.org/C158525013 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5683285593986511 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2593739 |
| concepts[5].display_name | Fusion |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.5218278765678406 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.47156593203544617 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C31972630 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3849810063838959 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[8].display_name | Computer vision |
| concepts[9].id | https://openalex.org/C58640448 |
| concepts[9].level | 1 |
| concepts[9].score | 0.2748289406299591 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q42515 |
| concepts[9].display_name | Cartography |
| concepts[10].id | https://openalex.org/C205649164 |
| concepts[10].level | 0 |
| concepts[10].score | 0.20434081554412842 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[10].display_name | Geography |
| concepts[11].id | https://openalex.org/C15744967 |
| concepts[11].level | 0 |
| concepts[11].score | 0.1924818456172943 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[11].display_name | Psychology |
| concepts[12].id | https://openalex.org/C169760540 |
| concepts[12].level | 1 |
| concepts[12].score | 0.05564883351325989 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[12].display_name | Neuroscience |
| concepts[13].id | https://openalex.org/C41895202 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[13].display_name | Linguistics |
| concepts[14].id | https://openalex.org/C138885662 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[14].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/feature |
| keywords[0].score | 0.7064210772514343 |
| keywords[0].display_name | Feature (linguistics) |
| keywords[1].id | https://openalex.org/keywords/segmentation |
| keywords[1].score | 0.6982797980308533 |
| keywords[1].display_name | Segmentation |
| keywords[2].id | https://openalex.org/keywords/scale |
| keywords[2].score | 0.6776217222213745 |
| keywords[2].display_name | Scale (ratio) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6167075037956238 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/perception |
| keywords[4].score | 0.6132376790046692 |
| keywords[4].display_name | Perception |
| keywords[5].id | https://openalex.org/keywords/fusion |
| keywords[5].score | 0.5683285593986511 |
| keywords[5].display_name | Fusion |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.5218278765678406 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.47156593203544617 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/computer-vision |
| keywords[8].score | 0.3849810063838959 |
| keywords[8].display_name | Computer vision |
| keywords[9].id | https://openalex.org/keywords/cartography |
| keywords[9].score | 0.2748289406299591 |
| keywords[9].display_name | Cartography |
| keywords[10].id | https://openalex.org/keywords/geography |
| keywords[10].score | 0.20434081554412842 |
| keywords[10].display_name | Geography |
| keywords[11].id | https://openalex.org/keywords/psychology |
| keywords[11].score | 0.1924818456172943 |
| keywords[11].display_name | Psychology |
| keywords[12].id | https://openalex.org/keywords/neuroscience |
| keywords[12].score | 0.05564883351325989 |
| keywords[12].display_name | Neuroscience |
| language | en |
| locations[0].id | doi:10.1088/1361-6560/acc71f |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S20241394 |
| locations[0].source.issn | 0031-9155, 1361-6560 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0031-9155 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Physics in Medicine and Biology |
| locations[0].source.host_organization | https://openalex.org/P4310320083 |
| locations[0].source.host_organization_name | IOP Publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| locations[0].source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Physics in Medicine & Biology |
| locations[0].landing_page_url | https://doi.org/10.1088/1361-6560/acc71f |
| locations[1].id | pmid:36958057 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| 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 | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Physics in medicine and biology |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/36958057 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5008614855 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Chenggang Lu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I184843921 |
| authorships[0].affiliations[0].raw_affiliation_string | Hebei University of Technology, Tianjin, China, Tianjin, 300401, CHINA |
| authorships[0].institutions[0].id | https://openalex.org/I184843921 |
| authorships[0].institutions[0].ror | https://ror.org/018hded08 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I184843921 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Hebei University of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chenggang Lu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Hebei University of Technology, Tianjin, China, Tianjin, 300401, CHINA |
| authorships[1].author.id | https://openalex.org/A5081188773 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0476-6180 |
| authorships[1].author.display_name | Jinli Yuan |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I184843921 |
| authorships[1].affiliations[0].raw_affiliation_string | Hebei University of Technology, Tianjin, China, Tianjin, 300401, CHINA |
| authorships[1].institutions[0].id | https://openalex.org/I184843921 |
| authorships[1].institutions[0].ror | https://ror.org/018hded08 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I184843921 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Hebei University of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jinli Yuan |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Hebei University of Technology, Tianjin, China, Tianjin, 300401, CHINA |
| authorships[2].author.id | https://openalex.org/A5068081929 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3968-481X |
| authorships[2].author.display_name | Kewen Xia |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I184843921 |
| authorships[2].affiliations[0].raw_affiliation_string | Hebei University of Technology, Tianjin, China, Tianjin, 300401, CHINA |
| authorships[2].institutions[0].id | https://openalex.org/I184843921 |
| authorships[2].institutions[0].ror | https://ror.org/018hded08 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I184843921 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Hebei University of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Kewen Xia |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Hebei University of Technology, Tianjin, China, Tianjin, 300401, CHINA |
| authorships[3].author.id | https://openalex.org/A5051746390 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-5398-2026 |
| authorships[3].author.display_name | Zhitao Guo |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I184843921 |
| authorships[3].affiliations[0].raw_affiliation_string | Hebei University of Technology, Tianjin, China, Tianjin, 300401, CHINA |
| authorships[3].institutions[0].id | https://openalex.org/I184843921 |
| authorships[3].institutions[0].ror | https://ror.org/018hded08 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I184843921 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Hebei University of Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zhitao Guo |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Hebei University of Technology, Tianjin, China, Tianjin, 300401, CHINA |
| authorships[4].author.id | https://openalex.org/A5070947571 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-8961-2649 |
| authorships[4].author.display_name | Muxuan Chen |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I184843921 |
| authorships[4].affiliations[0].raw_affiliation_string | Hebei University of Technology, Tianjin, Tianjin, Tianjin, 300401, CHINA |
| authorships[4].institutions[0].id | https://openalex.org/I184843921 |
| authorships[4].institutions[0].ror | https://ror.org/018hded08 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I184843921 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Hebei University of Technology |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Muxuan Chen |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Hebei University of Technology, Tianjin, Tianjin, Tianjin, 300401, CHINA |
| authorships[5].author.id | https://openalex.org/A5052662150 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5852-0813 |
| authorships[5].author.display_name | Hengyong Yu |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I133738476 |
| authorships[5].affiliations[0].raw_affiliation_string | Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell,Massachusetts, United States, Lowell, Massachusetts, 01854, UNITED STATES |
| authorships[5].institutions[0].id | https://openalex.org/I133738476 |
| authorships[5].institutions[0].ror | https://ror.org/03hamhx47 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I133738476 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | University of Massachusetts Lowell |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Hengyong Yu |
| authorships[5].is_corresponding | True |
| authorships[5].raw_affiliation_strings | Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell,Massachusetts, United States, Lowell, Massachusetts, 01854, UNITED STATES |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1088/1361-6560/acc71f |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Regional perception and multi-scale feature fusion network for cardiac segmentation |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| 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 | 0.9948999881744385 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Medical Image Segmentation Techniques |
| related_works | https://openalex.org/W2628861693, https://openalex.org/W4379231730, https://openalex.org/W3203087560, https://openalex.org/W4389858081, https://openalex.org/W2099421762, https://openalex.org/W2568121504, https://openalex.org/W3147584709, https://openalex.org/W2530546662, https://openalex.org/W4361279463, https://openalex.org/W2967030268 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1088/1361-6560/acc71f |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S20241394 |
| best_oa_location.source.issn | 0031-9155, 1361-6560 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0031-9155 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Physics in Medicine and Biology |
| best_oa_location.source.host_organization | https://openalex.org/P4310320083 |
| best_oa_location.source.host_organization_name | IOP Publishing |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| best_oa_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| best_oa_location.license | cc-by-nc-nd |
| 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-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Physics in Medicine & Biology |
| best_oa_location.landing_page_url | https://doi.org/10.1088/1361-6560/acc71f |
| primary_location.id | doi:10.1088/1361-6560/acc71f |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S20241394 |
| primary_location.source.issn | 0031-9155, 1361-6560 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0031-9155 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Physics in Medicine and Biology |
| primary_location.source.host_organization | https://openalex.org/P4310320083 |
| primary_location.source.host_organization_name | IOP Publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Physics in Medicine & Biology |
| primary_location.landing_page_url | https://doi.org/10.1088/1361-6560/acc71f |
| publication_date | 2023-03-23 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3011573465, https://openalex.org/W2011907650, https://openalex.org/W2274227799, https://openalex.org/W2804047627, https://openalex.org/W2890847992, https://openalex.org/W4321232185, https://openalex.org/W6790275670, https://openalex.org/W2896535407, https://openalex.org/W6755391574, https://openalex.org/W3022360411, https://openalex.org/W3039636409, https://openalex.org/W2955058313, https://openalex.org/W4212875960, https://openalex.org/W4313156423, https://openalex.org/W2752782242, https://openalex.org/W6729099515, https://openalex.org/W3102150723, https://openalex.org/W3012802901, https://openalex.org/W2132116135, https://openalex.org/W3138516171, https://openalex.org/W4308909683, https://openalex.org/W2962914239, https://openalex.org/W2790269399, https://openalex.org/W1966192780, https://openalex.org/W2948522751, https://openalex.org/W2103005517, https://openalex.org/W2057441779, https://openalex.org/W3023537913, https://openalex.org/W2059240422, https://openalex.org/W4205182665, https://openalex.org/W6832403244, https://openalex.org/W1901129140, https://openalex.org/W2033973976, https://openalex.org/W2606576226, https://openalex.org/W6703635231, https://openalex.org/W2137201504, https://openalex.org/W6739901393, https://openalex.org/W2117530999, https://openalex.org/W2034383786, https://openalex.org/W3212933375, https://openalex.org/W2109408432, https://openalex.org/W2618456149, https://openalex.org/W3134287839, https://openalex.org/W3206685025, https://openalex.org/W1966411054, https://openalex.org/W2774320778, https://openalex.org/W2082718270, https://openalex.org/W2543951688, https://openalex.org/W3127751679, https://openalex.org/W4385245566, https://openalex.org/W2337438617, https://openalex.org/W2893406581 |
| referenced_works_count | 52 |
| abstract_inverted_index.a | 6, 100, 113, 132, 139, 149, 164, 221, 269 |
| abstract_inverted_index.In | 34, 108 |
| abstract_inverted_index.We | 126 |
| abstract_inverted_index.an | 297 |
| abstract_inverted_index.by | 184 |
| abstract_inverted_index.in | 46, 83, 202, 258, 300 |
| abstract_inverted_index.is | 5, 97, 239, 263 |
| abstract_inverted_index.it | 87 |
| abstract_inverted_index.of | 8, 32, 39, 50, 57, 63, 158, 188, 210, 214, 293 |
| abstract_inverted_index.on | 243, 268 |
| abstract_inverted_index.to | 68, 72, 89, 98, 153, 160, 168, 197, 227, 231 |
| abstract_inverted_index.we | 111 |
| abstract_inverted_index.CMR | 35, 103, 123 |
| abstract_inverted_index.GET | 191 |
| abstract_inverted_index.Our | 95 |
| abstract_inverted_index.The | 145, 177, 190, 217, 237, 248, 261, 273 |
| abstract_inverted_index.WST | 146, 178 |
| abstract_inverted_index.and | 12, 15, 30, 74, 77, 116, 138, 163, 206, 295 |
| abstract_inverted_index.are | 22, 81 |
| abstract_inverted_index.can | 289 |
| abstract_inverted_index.for | 27, 122, 266 |
| abstract_inverted_index.its | 78 |
| abstract_inverted_index.the | 24, 28, 47, 54, 58, 61, 64, 75, 92, 156, 181, 186, 194, 199, 203, 208, 215, 229, 252, 278, 291 |
| abstract_inverted_index.two | 128 |
| abstract_inverted_index.CVD. | 33 |
| abstract_inverted_index.Main | 235 |
| abstract_inverted_index.This | 85 |
| abstract_inverted_index.also | 275 |
| abstract_inverted_index.data | 246, 271 |
| abstract_inverted_index.each | 174 |
| abstract_inverted_index.goal | 96 |
| abstract_inverted_index.gold | 25 |
| abstract_inverted_index.grid | 140 |
| abstract_inverted_index.left | 43 |
| abstract_inverted_index.maps | 196, 205 |
| abstract_inverted_index.more | 101 |
| abstract_inverted_index.play | 296 |
| abstract_inverted_index.role | 299 |
| abstract_inverted_index.set. | 272 |
| abstract_inverted_index.show | 250, 276 |
| abstract_inverted_index.that | 251, 277 |
| abstract_inverted_index.this | 109 |
| abstract_inverted_index.with | 66, 241 |
| abstract_inverted_index.(CMR) | 20 |
| abstract_inverted_index.(CVD) | 4 |
| abstract_inverted_index.(GET) | 143 |
| abstract_inverted_index.(WST) | 136 |
| abstract_inverted_index.(e.g. | 42 |
| abstract_inverted_index.block | 152, 167 |
| abstract_inverted_index.blood | 13 |
| abstract_inverted_index.grids | 193 |
| abstract_inverted_index.group | 7 |
| abstract_inverted_index.heart | 59, 65 |
| abstract_inverted_index.image | 104, 124, 287 |
| abstract_inverted_index.makes | 86 |
| abstract_inverted_index.novel | 222 |
| abstract_inverted_index.other | 69, 255, 281 |
| abstract_inverted_index.sets. | 247 |
| abstract_inverted_index.three | 244 |
| abstract_inverted_index.RMFNet | 218, 238, 253, 262, 279 |
| abstract_inverted_index.design | 127 |
| abstract_inverted_index.fusion | 119 |
| abstract_inverted_index.global | 170 |
| abstract_inverted_index.images | 21 |
| abstract_inverted_index.latent | 211 |
| abstract_inverted_index.module | 137, 147, 179, 192, 226 |
| abstract_inverted_index.reduce | 290 |
| abstract_inverted_index.retain | 232 |
| abstract_inverted_index.select | 155 |
| abstract_inverted_index.stress | 292 |
| abstract_inverted_index.study, | 110 |
| abstract_inverted_index.window | 133, 150, 157, 187 |
| abstract_inverted_index.within | 173 |
| abstract_inverted_index.ability | 230 |
| abstract_inverted_index.assists | 45 |
| abstract_inverted_index.beating | 56 |
| abstract_inverted_index.cardiac | 11, 17, 40, 51, 93, 245 |
| abstract_inverted_index.develop | 99 |
| abstract_inverted_index.disease | 3 |
| abstract_inverted_index.dynamic | 55 |
| abstract_inverted_index.enhance | 169 |
| abstract_inverted_index.feature | 118, 175, 195, 204, 212, 224 |
| abstract_inverted_index.further | 219, 264 |
| abstract_inverted_index.images, | 36 |
| abstract_inverted_index.images. | 94 |
| abstract_inverted_index.improve | 228 |
| abstract_inverted_index.medical | 286 |
| abstract_inverted_index.methods | 257 |
| abstract_inverted_index.module. | 144 |
| abstract_inverted_index.network | 120, 182 |
| abstract_inverted_index.overall | 259 |
| abstract_inverted_index.propose | 112 |
| abstract_inverted_index.renders | 60 |
| abstract_inverted_index.respect | 67 |
| abstract_inverted_index.results | 249, 274 |
| abstract_inverted_index.segment | 91 |
| abstract_inverted_index.similar | 82 |
| abstract_inverted_index.tissues | 70, 80 |
| abstract_inverted_index.window. | 176 |
| abstract_inverted_index.(RMFNet) | 121 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Accurate | 285 |
| abstract_inverted_index.However, | 53 |
| abstract_inverted_index.accurate | 37, 102 |
| abstract_inverted_index.advanced | 256 |
| abstract_inverted_index.clinical | 302 |
| abstract_inverted_index.decrease | 198 |
| abstract_inverted_index.detailed | 233 |
| abstract_inverted_index.diseases | 9 |
| abstract_inverted_index.enhances | 180, 207 |
| abstract_inverted_index.interest | 159 |
| abstract_inverted_index.location | 62 |
| abstract_inverted_index.magnetic | 18 |
| abstract_inverted_index.methods. | 283 |
| abstract_inverted_index.modules, | 131 |
| abstract_inverted_index.network. | 216 |
| abstract_inverted_index.perceive | 161 |
| abstract_inverted_index.regional | 114, 129 |
| abstract_inverted_index.resolve, | 73 |
| abstract_inverted_index.results. | 236 |
| abstract_inverted_index.standard | 26 |
| abstract_inverted_index.vessels, | 14 |
| abstract_inverted_index.windowed | 165 |
| abstract_inverted_index.Approach. | 107 |
| abstract_inverted_index.affecting | 10 |
| abstract_inverted_index.approach. | 106 |
| abstract_inverted_index.diagnosis | 29 |
| abstract_inverted_index.difficult | 71 |
| abstract_inverted_index.function. | 52 |
| abstract_inverted_index.important | 298 |
| abstract_inverted_index.improving | 185 |
| abstract_inverted_index.interest. | 189 |
| abstract_inverted_index.redundant | 200 |
| abstract_inverted_index.resonance | 19 |
| abstract_inverted_index.selection | 134, 151 |
| abstract_inverted_index.surpasses | 280 |
| abstract_inverted_index.validated | 240, 265 |
| abstract_inverted_index.Objective. | 1 |
| abstract_inverted_index.accurately | 90 |
| abstract_inverted_index.adaptively | 154 |
| abstract_inverted_index.assessment | 31 |
| abstract_inverted_index.comparison | 282 |
| abstract_inverted_index.considered | 23 |
| abstract_inverted_index.extraction | 141, 172, 209, 225 |
| abstract_inverted_index.grayscale. | 84 |
| abstract_inverted_index.introduces | 148, 220 |
| abstract_inverted_index.myocardium | 76 |
| abstract_inverted_index.parametric | 48 |
| abstract_inverted_index.perception | 115, 130 |
| abstract_inverted_index.short-axis | 16 |
| abstract_inverted_index.structures | 41 |
| abstract_inverted_index.ventricle) | 44 |
| abstract_inverted_index.challenging | 88 |
| abstract_inverted_index.experiments | 242 |
| abstract_inverted_index.information | 171, 201, 213 |
| abstract_inverted_index.multi-organ | 270 |
| abstract_inverted_index.multi-scale | 117, 223 |
| abstract_inverted_index.outperforms | 254 |
| abstract_inverted_index.performance | 183 |
| abstract_inverted_index.procedures. | 303 |
| abstract_inverted_index.surrounding | 79 |
| abstract_inverted_index.transformer | 135, 142, 166 |
| abstract_inverted_index.image-guided | 301 |
| abstract_inverted_index.information, | 162 |
| abstract_inverted_index.information. | 234 |
| abstract_inverted_index.performance. | 260 |
| abstract_inverted_index.radiologists | 294 |
| abstract_inverted_index.segmentation | 38, 105, 288 |
| abstract_inverted_index.Significance. | 284 |
| abstract_inverted_index.segmentation. | 125 |
| abstract_inverted_index.Cardiovascular | 2 |
| abstract_inverted_index.quantification | 49 |
| abstract_inverted_index.generalizability | 267 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5052662150, https://openalex.org/A5081188773 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I133738476, https://openalex.org/I184843921 |
| citation_normalized_percentile.value | 0.77716485 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |