Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms
We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-autom...
Saved in:
| Published in: | The international journal of cardiovascular imaging Vol. 38; no. 5; pp. 1047 - 1059 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Dordrecht
Springer Netherlands
01.05.2022
Springer Nature B.V |
| Subjects: | |
| ISSN: | 1875-8312, 1569-5794, 1875-8312, 1573-0743 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability. |
|---|---|
| AbstractList | We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability. We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability.We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability. |
| Author | Arsanjani, Reza Lee, Sang-Eun Yoo, Sun Kook Jeon, Jaeik Heo, Ran Moon, Inki Chang, Hyuk-Jae Kim, Sekeun Park, Hyung-Bok |
| Author_xml | – sequence: 1 givenname: Sekeun surname: Kim fullname: Kim, Sekeun organization: CONNECT-AI Research Center, Yonsei University College of Medicine, Graduate Program of Biomedical Engineering, Yonsei University College of Medicine – sequence: 2 givenname: Hyung-Bok surname: Park fullname: Park, Hyung-Bok organization: CONNECT-AI Research Center, Yonsei University College of Medicine, Department of Cardiology, Catholic Kwandong University International St. Mary’s Hospital – sequence: 3 givenname: Jaeik surname: Jeon fullname: Jeon, Jaeik organization: CONNECT-AI Research Center, Yonsei University College of Medicine – sequence: 4 givenname: Reza surname: Arsanjani fullname: Arsanjani, Reza organization: Department of Cardiovascular Diseases, Mayo Clinic Arizona – sequence: 5 givenname: Ran surname: Heo fullname: Heo, Ran organization: CONNECT-AI Research Center, Yonsei University College of Medicine, Department of Cardiology, Hanyang University Seoul Hospital, Hanyang University College of Medicine – sequence: 6 givenname: Sang-Eun surname: Lee fullname: Lee, Sang-Eun organization: CONNECT-AI Research Center, Yonsei University College of Medicine, Department of Cardiology, Ewha Womans University Seoul Hospital – sequence: 7 givenname: Inki surname: Moon fullname: Moon, Inki organization: CONNECT-AI Research Center, Yonsei University College of Medicine, Division of Cardiology, Department of Internal Medicine, Soonchunghyang University Bucheon Hospital – sequence: 8 givenname: Sun Kook surname: Yoo fullname: Yoo, Sun Kook email: sunkyoo@yuhs.ac organization: Department of Medical Engineering, Yonsei University College of Medicine – sequence: 9 givenname: Hyuk-Jae orcidid: 0000-0002-6139-7545 surname: Chang fullname: Chang, Hyuk-Jae email: hjchang@yuhs.ac organization: CONNECT-AI Research Center, Yonsei University College of Medicine, Division of Cardiology, Department of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Ontact Health Co., Ltd |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35152371$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kc1u3CAYRVGUKn_NC3RRIXXTjVv-bOzuqrRpK0XqJnv0GcMMEYYJ4IUfJW9bMpMoVRZZIJA454K-e46OQwwGoQ-UfKGEyK-ZkrYVDWG0LtGzZj1CZ7SXbdNzyo7_O5-i85zvCKGSD8MJOuUtbRmX9Aw9XC_erxiWEmcoZsL3C4TirNNQXAw4WqwhTQ401luYR5MwhAnbJej9PeRscp5NKNgFzJof2Oht3Ctxk2C3Xb9h7V2oeR5bA9mNzruyPgZPxuywN5CCC5tmhFyfB7-JyZXtnN-jdxZ8NpdP-wW6vf55e_W7ufn768_V95tGCzqUqg2t6GivGbPcCkKBC-DWdNz2uhu0EGKkZJzkaAc-MjNRQXqmuzoADdLwC_T5ELtL8X4xuajZZW28h2DikhXrWN_1Usquop9eoXdxSaF-rlJtN0gxSFKpj0_UMs5mUrvkZkirep55BfoDoFPMORmrtCv7aZcEzitK1GO96lCvqvWqfb1qrSp7pT6nvynxg5QrHDYmvXz7DesfCGe6YA |
| CitedBy_id | crossref_primary_10_1016_j_ultrasmedbio_2025_03_015 crossref_primary_10_7717_peerj_cs_3161 crossref_primary_10_1007_s10554_024_03095_x crossref_primary_10_1016_j_acvd_2025_04_051 crossref_primary_10_1016_j_artmed_2024_102866 crossref_primary_10_1111_echo_70290 crossref_primary_10_1117_1_JMI_12_2_024002 crossref_primary_10_3390_diagnostics14020150 crossref_primary_10_1007_s10554_022_02621_z crossref_primary_10_1016_j_compmedimag_2025_102627 crossref_primary_10_1053_j_jvca_2024_06_022 |
| Cites_doi | 10.7863/ultra.33.2.297 10.3390/diagnostics11071288 10.1046/j.0140-7783.2003.00543.x 10.1093/ehjci/jev014 10.1016/j.jacc.2012.09.035 10.1016/j.echo.2010.12.008 10.1109/TUFFC.2020.3003403 10.1016/j.echo.2020.04.025 10.1002/clc.22810 10.1097/XCE.0000000000000241 10.1016/j.echo.2004.03.021 10.1161/CIRCULATIONAHA.118.034338 10.4250/jcvi.2021.0039 10.1016/j.jacc.2018.12.054 10.1002/clc.22754 10.1161/CIRCIMAGING.119.010222 10.1109/JBHI.2019.2912935 10.1016/j.jcmg.2018.11.038 10.1016/j.jacc.2016.12.012 10.1016/j.isprsjprs.2020.01.013 10.1016/j.jacc.2015.07.052 10.1161/CIRCULATIONAHA.117.026622 10.1093/ejechocard/jep188 10.1007/s12574-020-00496-4 10.1093/ehjci/jey137 10.1098/rsif.2020.0267 10.1007/978-3-319-24574-4_28 10.1109/ICMLA.2018.00078 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2022 2022. The Author(s). The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2022 – notice: 2022. The Author(s). – notice: The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION NPM 3V. 7X7 7XB 88E 8AO 8FD 8FI 8FJ 8FK ABUWG AFKRA BENPR CCPQU FR3 FYUFA GHDGH K9. M0S M1P M7Z P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 |
| DOI | 10.1007/s10554-021-02482-y |
| DatabaseName | Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central ProQuest One Community College Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Biochemistry Abstracts 1 Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Biochemistry Abstracts 1 Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic Technology Research Database CrossRef |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1875-8312 1573-0743 |
| EndPage | 1059 |
| ExternalDocumentID | 35152371 10_1007_s10554_021_02482_y |
| Genre | Journal Article |
| GroupedDBID | --- -5E -5G -BR -EM -Y2 -~C .86 .GJ .VR 06C 06D 0R~ 0VY 1N0 203 29J 29~ 2J2 2JN 2JY 2KM 2LR 2P1 30V 3V. 4.4 406 408 409 40D 40E 53G 5GY 5VS 67Z 6NX 78A 7X7 88E 8AO 8FI 8FJ 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AAWTL AAYIU AAYZH ABAKF ABDZT ABECU ABFTV ABHLI ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABPLI ABQSL ABSXP ABTEG ABTKH ABTMW ABULA ABUWG ABXPI ACAOD ACGFS ACHSB ACIWK ACKNC ACMDZ ACMLO ACOKC ACPIV ACPRK ACUDM ACZOJ ADBBV ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEFQL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFKRA AFLOW AFRAH AFWTZ AFZKB AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKMHD ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AOCGG ARMRJ ASPBG AXYYD AZFZN B-. BA0 BDATZ BENPR BGNMA BPHCQ BVXVI C6C CAG CCPQU COF CS3 CSCUP DDRTE DNIVK DPUIP DU5 EBD EBLON EBS EIOEI EJD EMOBN ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GRRUI GXS HG5 HG6 HLICF HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IKXTQ IWAJR IXC IXD IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV LLZTM M1P M4Y MA- N2Q NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OVD P19 P2P P9S PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOR QOS R89 R9I RNI ROL RPX RRX RSV RZC RZE RZK S16 S26 S27 S28 S37 S3B SAP SCLPG SDH SDM SHX SISQX SJYHP SMD SNE SNPRN SNX SOHCF SOJ SRMVM SSLCW SSXJD SV3 SZ9 SZN T13 T16 TEORI TSG TSK TSV TT1 TUC U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VFIZW W23 W48 WJK WK8 YLTOR Z45 Z7U Z82 Z87 Z8O Z8V Z91 ZMTXR ZOVNA ~A9 AAPKM AAUYE AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC AEZWR AFDZB AFHIU AFOHR AHPBZ AHWEU AIXLP AYFIA CITATION ADHKG AGQPQ NPM PHGZM PHGZT PJZUB PPXIY 7XB 8FD 8FK FR3 K9. M7Z P64 PKEHL PQEST PQUKI PRINS 7X8 PUEGO |
| ID | FETCH-LOGICAL-c419t-ba954618c22f3f401a34a3fe63f8c69c444b10bd7bf93b2ed14082c6152ca7e3 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 12 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000754518800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1875-8312 1569-5794 |
| IngestDate | Thu Sep 04 17:26:24 EDT 2025 Wed Nov 05 00:47:01 EST 2025 Mon Jul 21 05:27:36 EDT 2025 Tue Nov 18 22:33:31 EST 2025 Sat Nov 29 01:53:38 EST 2025 Fri Feb 21 02:42:35 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | Deep learning Echocardiography Fully automated |
| Language | English |
| License | 2022. The Author(s). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c419t-ba954618c22f3f401a34a3fe63f8c69c444b10bd7bf93b2ed14082c6152ca7e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-6139-7545 |
| OpenAccessLink | https://link.springer.com/10.1007/s10554-021-02482-y |
| PMID | 35152371 |
| PQID | 2656974970 |
| PQPubID | 43205 |
| PageCount | 13 |
| ParticipantIDs | proquest_miscellaneous_2628687776 proquest_journals_2656974970 pubmed_primary_35152371 crossref_citationtrail_10_1007_s10554_021_02482_y crossref_primary_10_1007_s10554_021_02482_y springer_journals_10_1007_s10554_021_02482_y |
| PublicationCentury | 2000 |
| PublicationDate | 2022-05-01 |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Dordrecht |
| PublicationPlace_xml | – name: Dordrecht – name: United States |
| PublicationSubtitle | X-Ray Imaging, Intravascular Imaging, Echocardiography, Nuclear Cardiology, Computed Tomography and Magnetic Resonance Imaging |
| PublicationTitle | The international journal of cardiovascular imaging |
| PublicationTitleAbbrev | Int J Cardiovasc Imaging |
| PublicationTitleAlternate | Int J Cardiovasc Imaging |
| PublicationYear | 2022 |
| Publisher | Springer Netherlands Springer Nature B.V |
| Publisher_xml | – name: Springer Netherlands – name: Springer Nature B.V |
| References | Bethge, Penciu, Baksh, Parve, Lobraico, Keller (CR27) 2017; 40 Narang (CR17) 2019; 20 Collier, Phelan, Klein (CR30) 2017; 69 Lang (CR11) 2015; 16 Diakogiannis, Waldner, Caccetta, Wu (CR14) 2020; 162 Kusunose (CR8) 2021; 19 Abdelrazk, El-Sehrawy, Ghoniem, Amer (CR29) 2021; 10 Leclerc (CR20) 2020; 67 CR12 Douglas (CR4) 2011; 24 Knackstedt (CR16) 2015; 66 Thavendiranathan, Grant, Negishi, Plana, Popović, Marwick (CR2) 2013; 61 Guan, Khan, Sikdar, Chitnis (CR13) 2020; 24 Nolan, Thavendiranathan (CR19) 2019; 12 Chamsi-Pasha, Sengupta, Zoghbi (CR32) 2017; 136 Grossgasteiger (CR10) 2014; 33 Arafati (CR18) 2020 Cullen, Geske, Anavekar, Askew, Lewis, Oh (CR33) 2017; 40 Zhang (CR15) 2018; 138 Yoon, Kim, Chang (CR6) 2021; 29 Krishnamurthy, Daum, Langford (CR24) 2019; 20 Leclerc (CR7) 2019; 38 Wabich, Zienciuk-Krajka, Nowak, Raczak, Daniłowicz-Szymanowicz (CR31) 2021; 11 CR26 Huffer, Bauch, Furgerson, Bulgrin, Boyd (CR34) 2004; 17 Thorstensen, Dalen, Amundsen, Aase, Stoylen (CR1) 2010; 11 CR25 CR23 CR22 CR21 Dey (CR9) 2019; 73 Chetboul (CR3) 2004; 27 Davis (CR5) 2020; 33 Liu (CR28) 2020; 13 MA Chamsi-Pasha (2482_CR32) 2017; 136 Y Yoon (2482_CR6) 2021; 29 K Kusunose (2482_CR8) 2021; 19 S Guan (2482_CR13) 2020; 24 RM Lang (2482_CR11) 2015; 16 M Grossgasteiger (2482_CR10) 2014; 33 2482_CR12 S Leclerc (2482_CR7) 2019; 38 J Zhang (2482_CR15) 2018; 138 A Arafati (2482_CR18) 2020 FI Diakogiannis (2482_CR14) 2020; 162 S Leclerc (2482_CR20) 2020; 67 A Bethge (2482_CR27) 2017; 40 S Liu (2482_CR28) 2020; 13 E Wabich (2482_CR31) 2021; 11 V Chetboul (2482_CR3) 2004; 27 MT Nolan (2482_CR19) 2019; 12 P Thavendiranathan (2482_CR2) 2013; 61 2482_CR25 A Davis (2482_CR5) 2020; 33 2482_CR26 P Collier (2482_CR30) 2017; 69 D Dey (2482_CR9) 2019; 73 2482_CR21 2482_CR22 MW Cullen (2482_CR33) 2017; 40 2482_CR23 A Thorstensen (2482_CR1) 2010; 11 C Knackstedt (2482_CR16) 2015; 66 RR Abdelrazk (2482_CR29) 2021; 10 LL Huffer (2482_CR34) 2004; 17 PS Douglas (2482_CR4) 2011; 24 A Narang (2482_CR17) 2019; 20 A Krishnamurthy (2482_CR24) 2019; 20 |
| References_xml | – ident: CR22 – volume: 33 start-page: 297 issue: 2 year: 2014 end-page: 306 ident: CR10 article-title: Image quality influences the assessment of left ventricular function: an intraoperative comparison of five 2-dimensional echocardiographic methods with real-time 3-dimensional echocardiography as a reference publication-title: J Ultrasound Med doi: 10.7863/ultra.33.2.297 – volume: 11 start-page: 1288 issue: 7 year: 2021 ident: CR31 article-title: Comprehensive echocardiography of left atrium and left ventricle using modern techniques helps in better revealing atrial fibrillation in patients with hypertrophic cardiomyopathy publication-title: Diagnostics doi: 10.3390/diagnostics11071288 – volume: 27 start-page: 49 issue: 1 year: 2004 end-page: 56 ident: CR3 article-title: Observer-dependent variability of quantitative clinical endpoints: the example of canine echocardiography publication-title: J Vet Pharmacol Ther doi: 10.1046/j.0140-7783.2003.00543.x – volume: 16 start-page: 233 issue: 3 year: 2015 end-page: 271 ident: CR11 article-title: Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging publication-title: Eur Hear Journal-Cardiovascular Imaging doi: 10.1093/ehjci/jev014 – volume: 61 start-page: 77 issue: 1 year: 2013 end-page: 84 ident: CR2 article-title: Reproducibility of echocardiographic techniques for sequential assessment of left ventricular ejection fraction and volumes: Application to patients undergoing cancer chemotherapy publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2012.09.035 – ident: CR12 – volume: 24 start-page: 229 issue: 3 year: 2011 end-page: 267 ident: CR4 article-title: ACCF/ASE/AHA/ASNC/HFSA/HRS/SCAI/SCCM/SCCT/SCMR 2011 Appropriate use criteria for echocardiography publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2010.12.008 – volume: 20 start-page: 1 year: 2019 end-page: 50 ident: CR24 article-title: Active learning for cost-sensitive classification publication-title: J Mach Learn Res – volume: 67 start-page: 2519 issue: 12 year: 2020 end-page: 2530 ident: CR20 article-title: LU-Net: a multistage attention network to improve the robustness of segmentation of left ventricular structures in 2-D echocardiography publication-title: IEEE Trans Ultrason Ferroelectr Freq Control doi: 10.1109/TUFFC.2020.3003403 – volume: 33 start-page: 1061 issue: 9 year: 2020 end-page: 1066 ident: CR5 article-title: Artificial intelligence and echocardiography: a primer for cardiac sonographers publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2020.04.025 – ident: CR25 – ident: CR23 – ident: CR21 – volume: 40 start-page: 1212 issue: 12 year: 2017 end-page: 1217 ident: CR27 article-title: Appropriateness vs value: echocardiography in primary care publication-title: Clin Cardiol doi: 10.1002/clc.22810 – volume: 10 start-page: 182 issue: 3 year: 2021 end-page: 185 ident: CR29 article-title: Speckle tracking echocardiographic assessment of left ventricular longitudinal strain in female patients with subclinical hyperthyroidism publication-title: Cardiovasc Endocrinol Metab doi: 10.1097/XCE.0000000000000241 – volume: 17 start-page: 670 issue: 6 year: 2004 end-page: 674 ident: CR34 article-title: Feasibility of remote echocardiography with satellite transmission and real-time interpretation to support medical activities in the austere medical environment publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2004.03.021 – volume: 138 start-page: 1623 issue: 16 year: 2018 end-page: 1635 ident: CR15 article-title: Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.118.034338 – volume: 29 start-page: 193 issue: 3 year: 2021 end-page: 204 ident: CR6 article-title: Artificial intelligence and echocardiography publication-title: J Cardiovasc Imaging doi: 10.4250/jcvi.2021.0039 – volume: 73 start-page: 1317 issue: 11 year: 2019 end-page: 1335 ident: CR9 article-title: Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2018.12.054 – volume: 40 start-page: 993 issue: 11 year: 2017 end-page: 999 ident: CR33 article-title: Handheld echocardiography during hospitalization for acute myocardial infarction publication-title: Clin Cardiol doi: 10.1002/clc.22754 – volume: 13 start-page: 1 issue: 6 year: 2020 end-page: 4 ident: CR28 article-title: Left ventricular thrombus and heart failure with preserved ejection fraction in a patient with rheumatoid arthritis: a comprehensive assessment using serial echocardiography publication-title: Circ Cardiovasc Imaging doi: 10.1161/CIRCIMAGING.119.010222 – volume: 24 start-page: 568 issue: 2 year: 2020 end-page: 576 ident: CR13 article-title: Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2019.2912935 – volume: 12 start-page: 1073 issue: 6 year: 2019 end-page: 1092 ident: CR19 article-title: Automated quantification in echocardiography publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2018.11.038 – volume: 69 start-page: 1043 issue: 8 year: 2017 end-page: 1056 ident: CR30 article-title: A test in context: myocardial strain measured by speckle-tracking echocardiography publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2016.12.012 – volume: 38 start-page: 2019 issue: 2198–210 year: 2019 ident: CR7 article-title: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography publication-title: IEEE Trans Med Imaging – volume: 162 start-page: 94 year: 2020 end-page: 114 ident: CR14 article-title: ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data publication-title: ISPRS J Photogramm Remote Sens doi: 10.1016/j.isprsjprs.2020.01.013 – volume: 66 start-page: 1456 issue: 13 year: 2015 end-page: 1466 ident: CR16 article-title: Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain the FAST-EFs multicenter study publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2015.07.052 – volume: 136 start-page: 2178 issue: 22 year: 2017 end-page: 2188 ident: CR32 article-title: Handheld echocardiography: current state and future perspectives publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.117.026622 – volume: 11 start-page: 149 issue: 2 year: 2010 end-page: 156 ident: CR1 article-title: Reproducibility in echocardiographic assessment of the left ventricular global and regional function, the HUNT study publication-title: Eur J Echocardiogr doi: 10.1093/ejechocard/jep188 – volume: 19 start-page: 21 issue: 1 year: 2021 end-page: 27 ident: CR8 article-title: Steps to use artificial intelligence in echocardiography publication-title: J Echocardiogr doi: 10.1007/s12574-020-00496-4 – volume: 20 start-page: 541 issue: 5 year: 2019 end-page: 549 ident: CR17 article-title: Machine learning based automated dynamic quantification of left heart chamber volumes publication-title: Eur Heart J Cardiovasc Imaging doi: 10.1093/ehjci/jey137 – ident: CR26 – year: 2020 ident: CR18 article-title: Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks publication-title: J R Soc Interfaces doi: 10.1098/rsif.2020.0267 – volume: 40 start-page: 1212 issue: 12 year: 2017 ident: 2482_CR27 publication-title: Clin Cardiol doi: 10.1002/clc.22810 – volume: 69 start-page: 1043 issue: 8 year: 2017 ident: 2482_CR30 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2016.12.012 – volume: 13 start-page: 1 issue: 6 year: 2020 ident: 2482_CR28 publication-title: Circ Cardiovasc Imaging doi: 10.1161/CIRCIMAGING.119.010222 – volume: 12 start-page: 1073 issue: 6 year: 2019 ident: 2482_CR19 publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2018.11.038 – ident: 2482_CR12 doi: 10.1007/978-3-319-24574-4_28 – volume: 19 start-page: 21 issue: 1 year: 2021 ident: 2482_CR8 publication-title: J Echocardiogr doi: 10.1007/s12574-020-00496-4 – volume: 67 start-page: 2519 issue: 12 year: 2020 ident: 2482_CR20 publication-title: IEEE Trans Ultrason Ferroelectr Freq Control doi: 10.1109/TUFFC.2020.3003403 – volume: 61 start-page: 77 issue: 1 year: 2013 ident: 2482_CR2 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2012.09.035 – volume: 11 start-page: 149 issue: 2 year: 2010 ident: 2482_CR1 publication-title: Eur J Echocardiogr doi: 10.1093/ejechocard/jep188 – volume: 136 start-page: 2178 issue: 22 year: 2017 ident: 2482_CR32 publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.117.026622 – ident: 2482_CR22 – volume: 29 start-page: 193 issue: 3 year: 2021 ident: 2482_CR6 publication-title: J Cardiovasc Imaging doi: 10.4250/jcvi.2021.0039 – volume: 24 start-page: 568 issue: 2 year: 2020 ident: 2482_CR13 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2019.2912935 – volume: 138 start-page: 1623 issue: 16 year: 2018 ident: 2482_CR15 publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.118.034338 – volume: 33 start-page: 1061 issue: 9 year: 2020 ident: 2482_CR5 publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2020.04.025 – volume: 33 start-page: 297 issue: 2 year: 2014 ident: 2482_CR10 publication-title: J Ultrasound Med doi: 10.7863/ultra.33.2.297 – volume: 16 start-page: 233 issue: 3 year: 2015 ident: 2482_CR11 publication-title: Eur Hear Journal-Cardiovascular Imaging doi: 10.1093/ehjci/jev014 – volume: 24 start-page: 229 issue: 3 year: 2011 ident: 2482_CR4 publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2010.12.008 – volume: 20 start-page: 541 issue: 5 year: 2019 ident: 2482_CR17 publication-title: Eur Heart J Cardiovasc Imaging doi: 10.1093/ehjci/jey137 – volume: 162 start-page: 94 year: 2020 ident: 2482_CR14 publication-title: ISPRS J Photogramm Remote Sens doi: 10.1016/j.isprsjprs.2020.01.013 – year: 2020 ident: 2482_CR18 publication-title: J R Soc Interfaces doi: 10.1098/rsif.2020.0267 – ident: 2482_CR26 – volume: 27 start-page: 49 issue: 1 year: 2004 ident: 2482_CR3 publication-title: J Vet Pharmacol Ther doi: 10.1046/j.0140-7783.2003.00543.x – volume: 10 start-page: 182 issue: 3 year: 2021 ident: 2482_CR29 publication-title: Cardiovasc Endocrinol Metab doi: 10.1097/XCE.0000000000000241 – volume: 17 start-page: 670 issue: 6 year: 2004 ident: 2482_CR34 publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2004.03.021 – ident: 2482_CR25 – volume: 66 start-page: 1456 issue: 13 year: 2015 ident: 2482_CR16 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2015.07.052 – ident: 2482_CR21 – volume: 11 start-page: 1288 issue: 7 year: 2021 ident: 2482_CR31 publication-title: Diagnostics doi: 10.3390/diagnostics11071288 – volume: 20 start-page: 1 year: 2019 ident: 2482_CR24 publication-title: J Mach Learn Res – volume: 73 start-page: 1317 issue: 11 year: 2019 ident: 2482_CR9 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2018.12.054 – volume: 40 start-page: 993 issue: 11 year: 2017 ident: 2482_CR33 publication-title: Clin Cardiol doi: 10.1002/clc.22754 – volume: 38 start-page: 2019 issue: 2198–210 year: 2019 ident: 2482_CR7 publication-title: IEEE Trans Med Imaging – ident: 2482_CR23 doi: 10.1109/ICMLA.2018.00078 |
| SSID | ssj0017399 |
| Score | 2.3597932 |
| Snippet | We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the... |
| SourceID | proquest pubmed crossref springer |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1047 |
| SubjectTerms | Algorithms Annotations Automation Cardiac Imaging Cardiology Chambers Deep learning Echocardiography Heart Image acquisition Image segmentation Imaging Machine learning Medicine Medicine & Public Health Original Paper Patients Radiology Reproducibility Ventricle |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB5BQagXyrMsLchI3MDSxvbGCTcEVBygQlCh3qLxq12pJGU3i7Q_hX_L2OukoAISnDN2LHs831gz8w3AU-s8amWnXGA540qbkpupURyRsMbbUCKmQuF3-vCwOj6uP-SisOWQ7T6EJJOl_qnYjaCPx5SCyMMl-PoqXCO4q2LDho-fPo-xA02Ym8tjfj_uVwi65FdeiokmqDnY-b9F3oKb2bVkLze6cBuu-PYO3Hifg-d34Xt8bq4ZrvqO3FTv2NcVblKF0umwLjCb9MUye4qxUQjD1rGIfOk7jiSebN4ywV8zT7YzDcm81y_YUGjJgseceLuOEzvvz1nuUHHCI3Q6hmcn3WLen35Z3oOjgzdHr97y3JmBW1XUPYnVM1UWlRUiyEBPNJQKZfClDJUta6uUMsXUOG1CLY3wroh9rS15T8Ki9vI-bLVd6x8A815JE4x2PgRVKEdWwEicVW4WarIv5QSK4awam1nLY_OMs-aCbzlueUNb3qQtb9YTeDaOOd9wdvxVen9QgSbf32UjyM2ll1atpxN4Mn6mmxfDKdj6bhVlRFVGOkVa5O5GdcbfSXIThdTFBJ4PenIx-Z_X8vDfxPdgW8RqjJR_uQ9b_WLlH8F1-62fLxeP0634ASIJDFE priority: 102 providerName: Springer Nature |
| Title | Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms |
| URI | https://link.springer.com/article/10.1007/s10554-021-02482-y https://www.ncbi.nlm.nih.gov/pubmed/35152371 https://www.proquest.com/docview/2656974970 https://www.proquest.com/docview/2628687776 |
| Volume | 38 |
| WOSCitedRecordID | wos000754518800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1875-8312 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017399 issn: 1875-8312 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagRYgL5VVYKJWRuIHFxnbihAtqSysOsKpKhfYW-dlWKsm2m0Xan8K_ZcbrZIUqeuESKfIjjmY8D3tmPkLeWue1knbMuC5yJpUpmBkbybQGXeNtKLSOicJf1WRSTqfVcTpwm6ewyl4mRkHtWotn5B84GB5g-1Zq_Gl2xRA1Cm9XE4TGXbKJsNnI52o6OFyZEhE_ElyUiuXAeClpJqXOgSJlGKCAVb04W_6tmG5YmzduSqMCOtr636U_Ig-T6Un3VrzymNzxzRNy_1u6XH9KfqM7uqR60bVgxnpHrxZ6FUoUqUfbQG3kJ0vtuUYgEaobR1EzxnY9FPmkFw3l7DP1IFvjkFQX-yPtEzFp8DoF5i5xYuf9jCYEizOGqtVRfXkGf9Gd_5w_I6dHh6cHX1hCbmBWZlUH3apcFllpOQ8igAunhdQi-EKE0haVlVKabGycMqEShnuXIe61BeuKW6282CYbTdv4F4R6L4UJRjkfgsykAylhhM5Ll4cK5E8xIllPtdqmquYIrnFZr-sxI6VroHQdKV0vR-TdMGa2qulxa--dnqp12t_zek3SEXkzNMPOxOsW3fh2gX14WWC5RVjk8xUTDZ8TYEZyobIRed9z1Xryf6_l5e1reUUecMzOiPGYO2Sju1741-Se_dVdzK93496Iz3KXbO4fTo5P4O3k-48_SyQbNQ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFLVKQcCG92OggJFgRS0mthMnSAghStWq0xGLWcwu8rOtVJJpJwPKp_AR_CPXjpMRquiuC9Z-xLKP7yO-9x6E3mhjpeB6TKjMUsKFyogaK06kBF1jtcukDInCEzGd5vN58W0D_e5zYXxYZS8Tg6A2tfb_yN9TMDzA9i3E-NPijHjWKP-62lNodLA4sO1PcNmWH_d34HzfUrr7dfZlj0RWAaJ5UjREySLlWZJrSh1z4F5IxiVzNmMu11mhOecqGSsjlCuYotYknpNZg-anWgrLYNpr6DqIceF9PTEf_LtEsEBXCR5RQVLAeczRiZl6oLeJj4fwRcQoaf_WgxeM2wsPs0Hf7d79z3bqHroTDWv8ubsJ99GGrR6gm4cxdOAh-uWd7RbLVVODkW4NPlvJLlAqYBPXDutwWzTWx9LTpGBZGez1fmiXQwlTfFJhSnawBc0RhsSq3x9wn2aKnZUx7Lj1ExtrFzjycxwRbzgYLE-PYNOa4-_LR2h2FfvyGG1WdWWfImwtZ8opYaxzPOEGZKBiMs1N6gqQrtkIJT1ISh1rtnvqkNNyXW3aA6sEYJUBWGU7Qu-GMYuuYsmlvbd6EJVRei3LNYJG6PXQDHLHPybJytYr34fmmS8mCYt80mF2-BwDI5kykYzQdg_i9eT_Xsuzy9fyCt3amx1Oysn-9OA5uk19HkqIPN1Cm835yr5AN_SP5mR5_jJcS4zKKwb3Hw-xc2o |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFL0qU1SxKc_CQAEjwQqsThznhYQQMB1RtYxGqIvuIj_bSiWZdjJF-RQ-hb_jOnEyQhXddcE2fsRyju89zn0BvFbaiISrEWUijihPZEzlSHIqBOoao2wsRBMofJBMp-nRUTZbg99dLIxzq-xkYiOodancP_IdhsQDuW-WjHasd4uYjScf5-fUVZByltaunEYLkX1T_8Tr2-LD3hi_9RvGJruHX75SX2GAKh5kFZUii3gcpIoxG1q8aoiQi9CaOLSpijPFOZfBSOpE2iyUzOjA1WdWyAKYEokJcdpbsJ4gx-ADWP-8O519700Y-DRrkrXGGY0Q9T5ix8ftoRanzjvCpRRjtP5bK16hulfMtI32m9z9j_ftHmx6yk0-tWfkPqyZ4gFsfPNOBQ_hl7uG10QsqxLpu9HkfClaF6oGtaS0RDXnSBF1IlwBFSIKTRwjaNpFn9yUnBaE0TExqFOaIT4f-HvSBaASa4R3SK7dxNqYOfGVO46poxSaiLNj3LTq5MfiERzexL5swaAoC_MEiDE8lFYm2ljLA65ROspQRKmObIZyNx5C0AEmVz6buysqcpav8lA7kOUIsrwBWV4P4W0_Zt7mMrm293YHqNzLtUW-QtMQXvXNKJGcmUkUply6PiyNXZpJXOTjFr_960KkzyxMgiG86wC9mvzfa3l6_VpewgZiOj_Ym-4_gzvMBag0LqnbMKguluY53FaX1eni4oU_owTyG0b3H9BNfYo |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fully+automated+quantification+of+cardiac+chamber+and+function+assessment+in+2-D+echocardiography%3A+clinical+feasibility+of+deep+learning-based+algorithms&rft.jtitle=The+international+journal+of+cardiovascular+imaging&rft.au=Kim%2C+Sekeun&rft.au=Park%2C+Hyung-Bok&rft.au=Jeon%2C+Jaeik&rft.au=Arsanjani%2C+Reza&rft.date=2022-05-01&rft.issn=1875-8312&rft.eissn=1875-8312&rft.volume=38&rft.issue=5&rft.spage=1047&rft_id=info:doi/10.1007%2Fs10554-021-02482-y&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1875-8312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1875-8312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1875-8312&client=summon |