Deep learning pipeline for fully automated myocardial infarct segmentation from clinical cardiac MR scans
Saved in:
| Published in | Radiology advances Vol. 2; no. 4 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
03.07.2025
|
| Online Access | Get full text |
| ISSN | 2976-9337 2976-9337 |
| DOI | 10.1093/radadv/umaf023 |
Cover
| Author | Mayr, Agnes Kremser, Christian Pamminger, Mathias Haltmeier, Markus Schwab, Matthias |
|---|---|
| Author_xml | – sequence: 1 givenname: Matthias surname: Schwab fullname: Schwab, Matthias – sequence: 2 givenname: Mathias orcidid: 0000-0001-9241-6334 surname: Pamminger fullname: Pamminger, Mathias – sequence: 3 givenname: Christian surname: Kremser fullname: Kremser, Christian – sequence: 4 givenname: Markus surname: Haltmeier fullname: Haltmeier, Markus – sequence: 5 givenname: Agnes orcidid: 0000-0001-9363-873X surname: Mayr fullname: Mayr, Agnes |
| BookMark | eNpNkE9LAzEUxINUsNZePecLbJvdbDabo9Q_FSqCel8eeS8lspssyVbot7fSHjzNHGYG5nfLZiEGYuy-FKtSGLlOgIA_68MATlTyis0ro5vCSKln__wNW-b8LYSQUmhdmznzj0Qj7wlS8GHPRz9S7wNxFxN3h74_cjhMcYCJkA_HaCGhh5774CDZiWfaDxQmmHwM3KU4cHuqe3uKnKOWv33wbCHkO3btoM-0vOiCfT4_fW22xe795XXzsCusNrIwiJoUOFSmxZqQ2raVCixWSmpE1YCzSGhr0tYpiboWTak0ico17enWgq3OqzbFnBO5bkx-gHTsStH9kerOpLoLKfkLUDFjfg |
| Cites_doi | 10.1016/j.media.2025.103594 10.1016/j.patcog.2009.03.008 10.1007/978-3-030-32245-8_62 10.1093/eurheartj/ehab621 10.1016/S0140-6736 10.1093/neuonc/noab071 10.1007/s00234-021-02649-3 10.1117/1.JMI.7.5.055501 10.1016/j.cvdhj.2021.11.007 10.1056/NEJM200011163432003 10.1109/TMI.2018.2791721 10.1002/jmri.21498 10.59275/j.melba.2023-dg1f 10.1016/j.jacc.2010.02.033 10.1186/1532-429X-11-33 10.1093/eurheartj/ehx414 10.1186/s12968-020-00695-z 10.1007/s10334-018-0718-4 10.1016/S0378-3758 10.1161/CIRCOUTCOMES.118.005375 10.2307/2532051 10.1016/j.jacc.2011.09.073 10.1148/ryai.220231 10.2307/2529310 10.1016/j.jcmg.2010.11.015 10.1016/j.jcmg.2022.02.010 10.1148/radiol.2019190737 10.1016/j.bspc.2023.105710 10.1109/ICIP.2014.7025904 10.3390/s22062084 10.1016/j.ijcard.2022.05.009 10.1016/j.media.2023.102808 10.1161/01.CIR.100.19.1992 10.1186/s12968-022-00888-8 10.1016/j.media.2022.102428 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION |
| DOI | 10.1093/radadv/umaf023 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2976-9337 |
| ExternalDocumentID | 10_1093_radadv_umaf023 |
| GroupedDBID | 0R~ AAYXX ABEJV ABGNP ABXVV ALMA_UNASSIGNED_HOLDINGS AMNDL CITATION GROUPED_DOAJ M~E TOX |
| ID | FETCH-LOGICAL-c793-9dd7e5afd598d4ede88835acd2537dd56afcdedc4e7cf53d7406157e02f68033 |
| ISSN | 2976-9337 |
| IngestDate | Thu Jul 31 00:36:18 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c793-9dd7e5afd598d4ede88835acd2537dd56afcdedc4e7cf53d7406157e02f68033 |
| ORCID | 0000-0001-9363-873X 0000-0001-9241-6334 |
| OpenAccessLink | https://doi.org/10.1093/radadv/umaf023 |
| ParticipantIDs | crossref_primary_10_1093_radadv_umaf023 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-07-03 |
| PublicationDateYYYYMMDD | 2025-07-03 |
| PublicationDate_xml | – month: 07 year: 2025 text: 2025-07-03 day: 03 |
| PublicationDecade | 2020 |
| PublicationTitle | Radiology advances |
| PublicationYear | 2025 |
| References | Yue (2025072814265352600_umaf023-B10) 2019 Shi (2025072814265352600_umaf023-B19) Lu (2025072814265352600_umaf023-B20) 2021; 23 Al-antari (2025072814265352600_umaf023-B13) 2024; 89 Wang (2025072814265352600_umaf023-B21) 2018; 37 Nowbar (2025072814265352600_umaf023-B1) 2019; 12 Di Ieva (2025072814265352600_umaf023-B22) 2021; 63 Brahim (2025072814265352600_umaf023-B11) 2022; 22 Lalande (2025072814265352600_umaf023-B14) 2022; 79 Kim (2025072814265352600_umaf023-B3) 2000; 343 Popescu (2025072814265352600_umaf023-B12) 2022; 3 Li (2025072814265352600_umaf023-B15) 2023; 87 Mather (2025072814265352600_umaf023-B34) 2009; 11 Heiberg (2025072814265352600_umaf023-B33) 2022; 24 Gerber (2025072814265352600_umaf023-B4) 2012; 59 Fahmy (2025072814265352600_umaf023-B9) 2020; 294 Kim (2025072814265352600_umaf023-B35) 1999; 100 Lechner (2025072814265352600_umaf023-B25) 2022; 43 Klug (2025072814265352600_umaf023-B27) 2009; 29 Mayr (2025072814265352600_umaf023-B24) 2022; 15 Fadil (2025072814265352600_umaf023-B32) 2021; 23 Flett (2025072814265352600_umaf023-B7) 2011; 4 Mitchell (2025072814265352600_umaf023-B23) 2020; 7 Moccia (2025072814265352600_umaf023-B8) 2019; 32 Kofler (2025072814265352600_umaf023-B16) 2023; 2(May 2023) Vos (2025072814265352600_umaf023-B2) 2020; 396 Schwab (2025072814265352600_umaf023-B28) 2025; 103 Lechner (2025072814265352600_umaf023-B26) 2022; 361 de Waha (2025072814265352600_umaf023-B6) 2017; 38 Lawrence (2025072814265352600_umaf023-B29) 1989; 45 Hoebel (2025072814265352600_umaf023-B17) 2024; 6 Landis (2025072814265352600_umaf023-B31) 1977; 33 Larose (2025072814265352600_umaf023-B5) 2010; 55 McGuinness (2025072814265352600_umaf023-B18) 2010; 43 Glaz (2025072814265352600_umaf023-B30) 1999; 82 |
| References_xml | – volume: 103 start-page: 103594 year: 2025 ident: 2025072814265352600_umaf023-B28 article-title: Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images publication-title: Med Image Anal doi: 10.1016/j.media.2025.103594 – volume: 43 start-page: 434 issue: 2 year: 2010 ident: 2025072814265352600_umaf023-B18 article-title: A comparative evaluation of interactive segmentation algorithms publication-title: Pattern Recognit doi: 10.1016/j.patcog.2009.03.008 – start-page: 559 volume-title: Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 year: 2019 ident: 2025072814265352600_umaf023-B10 doi: 10.1007/978-3-030-32245-8_62 – volume: 43 start-page: 1141 issue: 11 year: 2022 ident: 2025072814265352600_umaf023-B25 article-title: Impact of COVID-19 pandemic restrictions on ST-elevation myocardial infarction: a cardiac magnetic resonance imaging study publication-title: Eur Heart J doi: 10.1093/eurheartj/ehab621 – volume: 396 start-page: 1204 issue: 10258 year: 2020 ident: 2025072814265352600_umaf023-B2 article-title: Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 publication-title: Lancet doi: 10.1016/S0140-6736 – volume: 23 start-page: 1560 issue: 9 year: 2021 ident: 2025072814265352600_umaf023-B20 article-title: Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks publication-title: Neuro Oncol doi: 10.1093/neuonc/noab071 – volume: 63 start-page: 1253 issue: 8 year: 2021 ident: 2025072814265352600_umaf023-B22 article-title: Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario publication-title: Neuroradiology doi: 10.1007/s00234-021-02649-3 – volume: 7 start-page: 055501 issue: 5 year: 2020 ident: 2025072814265352600_umaf023-B23 article-title: Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data publication-title: J Med Imaging (Bellingham) doi: 10.1117/1.JMI.7.5.055501 – volume: 3 start-page: 2 issue: 1 year: 2022 ident: 2025072814265352600_umaf023-B12 article-title: Anatomically informed deep learning on contrast-enhanced cardiac magnetic resonance imaging for scar segmentation and clinical feature extraction publication-title: Cardiovasc Digit Health J doi: 10.1016/j.cvdhj.2021.11.007 – volume: 343 start-page: 1445 issue: 20 year: 2000 ident: 2025072814265352600_umaf023-B3 article-title: The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction publication-title: N Engl J Med doi: 10.1056/NEJM200011163432003 – volume: 37 start-page: 1562 issue: 7 year: 2018 ident: 2025072814265352600_umaf023-B21 article-title: Interactive medical image segmentation using deep learning with image-specific fine tuning publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2018.2791721 – volume: 29 start-page: 298 issue: 2 year: 2009 ident: 2025072814265352600_umaf023-B27 article-title: Quantification of regional functional improvement of infarcted myocardium after primary PTCA by contrast-enhanced magnetic resonance imaging publication-title: J Magn Reson Imaging doi: 10.1002/jmri.21498 – volume: 2(May 2023) start-page: 27 year: 2023 ident: 2025072814265352600_umaf023-B16 article-title: Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient publication-title: J Mach Learn Biomed Imaging doi: 10.59275/j.melba.2023-dg1f – volume: 55 start-page: 2459 issue: 22 year: 2010 ident: 2025072814265352600_umaf023-B5 article-title: Predicting late myocardial recovery and outcomes in the early hours of ST-segment elevation myocardial infarction: traditional measures compared with microvascular obstruction, salvaged myocardium, and necrosis characteristics by cardiovascular magnetic resonance publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2010.02.033 – volume: 11 start-page: 33 issue: 1 year: 2009 ident: 2025072814265352600_umaf023-B34 article-title: Appearance of microvascular obstruction on high resolution first-pass perfusion, early and late gadolinium enhancement CMR in patients with acute myocardial infarction publication-title: J Cardiovasc Magn Reson doi: 10.1186/1532-429X-11-33 – volume: 38 start-page: 3502 issue: 47 year: 2017 ident: 2025072814265352600_umaf023-B6 article-title: Relationship between microvascular obstruction and adverse events following primary percutaneous coronary intervention for ST-segment elevation myocardial infarction: an individual patient data pooled analysis from seven randomized trials publication-title: Eur Heart J doi: 10.1093/eurheartj/ehx414 – volume: 23 start-page: 47 issue: 1 year: 2021 ident: 2025072814265352600_umaf023-B32 article-title: A deep learning pipeline for automatic analysis of multi-scan cardiovascular magnetic resonance publication-title: J Cardiovasc Magn Reson doi: 10.1186/s12968-020-00695-z – volume: 32 start-page: 187 issue: 2 year: 2019 ident: 2025072814265352600_umaf023-B8 article-title: Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images publication-title: MAGMA doi: 10.1007/s10334-018-0718-4 – volume: 82 start-page: 251 issue: 1-2 year: 1999 ident: 2025072814265352600_umaf023-B30 article-title: Simultaneous confidence intervals for multinomial proportions publication-title: J Stat Plan Inference doi: 10.1016/S0378-3758 – volume: 12 start-page: e005375 issue: 6 year: 2019 ident: 2025072814265352600_umaf023-B1 article-title: Mortality from ischemic heart disease: analysis of data from the World Health Organization and coronary artery disease risk factors from NCD risk factor collaboration publication-title: Circ Cardiovasc Qual Outcomes doi: 10.1161/CIRCOUTCOMES.118.005375 – volume: 45 start-page: 255 issue: 1 year: 1989 ident: 2025072814265352600_umaf023-B29 article-title: A concordance correlation coefficient to evaluate reproducibility publication-title: Biometrics doi: 10.2307/2532051 – volume: 59 start-page: 825 issue: 9 year: 2012 ident: 2025072814265352600_umaf023-B4 article-title: Prognostic value of myocardial viability by delayed-enhanced magnetic resonance in patients with coronary artery disease and low ejection fraction: impact of revascularization therapy publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2011.09.073 – volume: 6 start-page: e220231 issue: 1 year: 2024 ident: 2025072814265352600_umaf023-B17 article-title: Expert-centered evaluation of deep learning algorithms for brain tumor segmentation publication-title: Radiol Artif Intell doi: 10.1148/ryai.220231 – volume: 33 start-page: 159 issue: 1 year: 1977 ident: 2025072814265352600_umaf023-B31 article-title: The measurement of observer agreement for categorical data publication-title: Biometrics doi: 10.2307/2529310 – volume: 4 start-page: 150 issue: 2 year: 2011 ident: 2025072814265352600_umaf023-B7 article-title: Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2010.11.015 – volume: 15 start-page: 1030 issue: 6 year: 2022 ident: 2025072814265352600_umaf023-B24 article-title: Evolution of myocardial tissue injury: a CMR study over a decade after STEMI publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2022.02.010 – volume: 294 start-page: 52 issue: 1 year: 2020 ident: 2025072814265352600_umaf023-B9 article-title: Three-dimensional deep convolutional neural networks for automated myocardial scar quantification in hypertrophic cardiomyopathy: a multicenter multivendor study publication-title: Radiology doi: 10.1148/radiol.2019190737 – volume: 89 start-page: 105710 year: 2024 ident: 2025072814265352600_umaf023-B13 article-title: Deep learning myocardial infarction segmentation framework from cardiac magnetic resonance images publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2023.105710 – ident: 2025072814265352600_umaf023-B19 doi: 10.1109/ICIP.2014.7025904 – volume: 22 start-page: 2084 issue: 6 year: 2022 ident: 2025072814265352600_umaf023-B11 article-title: An improved 3D deep learning-based segmentation of left ventricular myocardial diseases from delayed-enhancement MRI with inclusion and classification prior information U-Net (ICPIU-Net) publication-title: Sensors doi: 10.3390/s22062084 – volume: 361 start-page: 1 year: 2022 ident: 2025072814265352600_umaf023-B26 article-title: Association between inflammation and left ventricular thrombus formation following ST-elevation myocardial infarction publication-title: Int J Cardiol doi: 10.1016/j.ijcard.2022.05.009 – volume: 87 start-page: 102808 year: 2023 ident: 2025072814265352600_umaf023-B15 article-title: MyoPS: a benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images publication-title: Med Image Anal doi: 10.1016/j.media.2023.102808 – volume: 100 start-page: 1992 issue: 19 year: 1999 ident: 2025072814265352600_umaf023-B35 article-title: Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function publication-title: Circulation doi: 10.1161/01.CIR.100.19.1992 – volume: 24 start-page: 53 issue: 1 year: 2022 ident: 2025072814265352600_umaf023-B33 article-title: Infarct quantification with cardiovascular magnetic resonance using “standard deviation from remote” is unreliable: validation in multi-centre multi-vendor data publication-title: J Cardiovasc Magn Reson doi: 10.1186/s12968-022-00888-8 – volume: 79 start-page: 102428 year: 2022 ident: 2025072814265352600_umaf023-B14 article-title: Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge publication-title: Med Image Anal doi: 10.1016/j.media.2022.102428 |
| SSID | ssj0003307749 |
| Score | 2.3001053 |
| SourceID | crossref |
| SourceType | Index Database |
| Title | Deep learning pipeline for fully automated myocardial infarct segmentation from clinical cardiac MR scans |
| Volume | 2 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELaW9sIFFQEC-pAPSByitCs7zuNYFVAFKkLbVuptNfGju4JsV9msqvbQA7-c8SOPoj0ULlFkJ07i-TL-ZjIzIeSDSQ2TUEI8TmUZJzm-c4DmcowMCTLJuC2RZaMtvqenl8nXK3E1Gv0eRC2tm_JQ3m_MK_kfqWIbytVmyf6DZLtBsQH3Ub64RQnj9kky_qT1sv3vw3W0nC-1I402ctC61e8iWDc3SEmRVFZ3uGjVLksEr4zobqKVvq5C5tHCp5l0aZL-UBmdTaIVzv1qSGEnoNrCTT5-oGPl53J2C6XPAGqa2Ry6nh9QVc5_GDqHfd9qXa1COuIsqJxeL_5qKj1vz6t_PnZTMOFCWnmvzRjynrjgvsLLod7QFtQxG6Au2ajkfQGsGhQ-Ju6sKzBjn7T8uJ72X-tcF33ov7vzqR9hGs5_RrZZhvzLhn8-9H46jkowc1ZUd7dd7U9-5Ic4CkMMuM2ApFzskBfBuqDHHiovyUgvXpG5hQltYUJbmFCECXUwoR1MaA8TGmBChzChFia0hQkNMKFnE-pg8pqcf_l8cXIahz9sxBL1clwolWkBRokiV4lWOs-RkINUTPBMKZGCkUormehMGsFVZtmfyPSYmTTHmXlDthY3C_2WUDTbQRhmjIE8KcCUpSjcF75CcoU2_zvysZ2Z6dKXUZluFsP7Jx-5S573QNsjW0291vvIEJvywHlWDpwg_wBYPnQr |
| linkProvider | ISSN International Centre |
| 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=Deep+learning+pipeline+for+fully+automated+myocardial+infarct+segmentation+from+clinical+cardiac+MR+scans&rft.jtitle=Radiology+advances&rft.au=Schwab%2C+Matthias&rft.au=Pamminger%2C+Mathias&rft.au=Kremser%2C+Christian&rft.au=Haltmeier%2C+Markus&rft.date=2025-07-03&rft.issn=2976-9337&rft.eissn=2976-9337&rft.volume=2&rft.issue=4&rft_id=info:doi/10.1093%2Fradadv%2Fumaf023&rft.externalDBID=n%2Fa&rft.externalDocID=10_1093_radadv_umaf023 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2976-9337&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2976-9337&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2976-9337&client=summon |