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Sökning: id:"swepub:oai:lup.lub.lu.se:3ac53ce5-5f4e-4815-8c37-252b6bb7ec2f" > MVnet : automated t...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00006451naa a2200469 4500
001oai:lup.lub.lu.se:3ac53ce5-5f4e-4815-8c37-252b6bb7ec2f
003SwePub
008221021s2021 | |||||||||||000 ||eng|
0247 a https://lup.lub.lu.se/record/3ac53ce5-5f4e-4815-8c37-252b6bb7ec2f2 URI
0247 a https://doi.org/10.1186/s12968-021-00824-22 DOI
040 a (SwePub)lu
041 a engb eng
042 9 SwePub
072 7a art2 swepub-publicationtype
072 7a ref2 swepub-contenttype
1001 a Gonzales, Ricardo A.u Lund University,Lunds universitet,Klinisk fysiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Clinical Physiology (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Yale University,Universidad de Ingeniería y Tecnología (UTEC)4 aut0 (Swepub:lu)ri2341go
2451 0a MVnet : automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
264 c 2021-12-02
264 1b Springer Science and Business Media LLC,c 2021
520 a Background: Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e’) are key metrics of systolic and diastolic function, but not often measured by cardiovascular magnetic resonance (CMR). Its derivation is possible with manual, precise annotation of the mitral valve (MV) insertion points along the cardiac cycle in both two and four-chamber long-axis cines, but this process is highly time-consuming, laborious, and prone to errors. A fully automated, consistent, fast, and accurate method for MV plane tracking is lacking. In this study, we propose MVnet, a deep learning approach for MV point localization and tracking capable of deriving such clinical metrics comparable to human expert-level performance, and validated it in a multi-vendor, multi-center clinical population. Methods: The proposed pipeline first performs a coarse MV point annotation in a given cine accurately enough to apply an automated linear transformation task, which standardizes the size, cropping, resolution, and heart orientation, and second, tracks the MV points with high accuracy. The model was trained and evaluated on 38,854 cine images from 703 patients with diverse cardiovascular conditions, scanned on equipment from 3 main vendors, 16 centers, and 7 countries, and manually annotated by 10 observers. Agreement was assessed by the intra-class correlation coefficient (ICC) for both clinical metrics and by the distance error in the MV plane displacement. For inter-observer variability analysis, an additional pair of observers performed manual annotations in a randomly chosen set of 50 patients. Results: MVnet achieved a fast segmentation (<1 s/cine) with excellent ICCs of 0.94 (MAPSE) and 0.93 (LV e’) and a MV plane tracking error of −0.10 ± 0.97 mm. In a similar manner, the inter-observer variability analysis yielded ICCs of 0.95 and 0.89 and a tracking error of −0.15 ± 1.18 mm, respectively. Conclusion: A dual-stage deep learning approach for automated annotation of MV points for systolic and diastolic evaluation in CMR long-axis cine images was developed. The method is able to carefully track these points with high accuracy and in a timely manner. This will improve the feasibility of CMR methods which rely on valve tracking and increase their utility in a clinical setting.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng
653 a Annotation
653 a Left ventricular dysfunction
653 a Residual neural networks
7001 a Seemann, Feliciau Lund University,Lunds universitet,Avdelningen för Biomedicinsk teknik,Institutionen för biomedicinsk teknik,Institutioner vid LTH,Lunds Tekniska Högskola,Klinisk fysiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Department of Biomedical Engineering,Departments at LTH,Faculty of Engineering, LTH,Clinical Physiology (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Yale University4 aut0 (Swepub:lu)med-fse
7001 a Lamy, Jérômeu Yale University4 aut
7001 a Mojibian, Hamidu Yale University4 aut
7001 a Atar, Danu Oslo university hospital4 aut
7001 a Erlinge, Davidu Lund University,Lunds universitet,Molekylär kardiologi,Forskargrupper vid Lunds universitet,Molecular Cardiology,Lund University Research Groups,Skåne University Hospital4 aut0 (Swepub:lu)kard-der
7001 a Steding-Ehrenborg, Katarinau Lund University,Lunds universitet,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,Lund Cardiac MR Group,Lund University Research Groups,Skåne University Hospital4 aut0 (Swepub:lu)med-kns
7001 a Arheden, Håkanu Lund University,Lunds universitet,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,Lund Cardiac MR Group,Lund University Research Groups,Skåne University Hospital4 aut0 (Swepub:lu)klfy-har
7001 a Hu, Chenxiu Shanghai Jiao Tong University4 aut
7001 a Onofrey, John A.u Yale University4 aut
7001 a Peters, Dana C.u Yale University4 aut
7001 a Heiberg, Einaru Lund University,Lunds universitet,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,Medicinska fakulteten,LTH profilområde: Teknik för hälsa,LTH profilområden,Lunds Tekniska Högskola,Lund Cardiac MR Group,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,Faculty of Medicine,LTH Profile Area: Engineering Health,LTH Profile areas,Faculty of Engineering, LTH,Skåne University Hospital4 aut0 (Swepub:lu)klin-eh0
7102 a Klinisk fysiologi, Lundb Sektion V4 org
7730 t Journal of Cardiovascular Magnetic Resonanced : Springer Science and Business Media LLCg 23, s. 1-15q 23<1-15x 1097-6647x 1532-429X
8564 u http://dx.doi.org/10.1186/s12968-021-00824-2x freey FULLTEXT
8564 u https://jcmr-online.biomedcentral.com/track/pdf/10.1186/s12968-021-00824-2
8564 8u https://lup.lub.lu.se/record/3ac53ce5-5f4e-4815-8c37-252b6bb7ec2f
8564 8u https://doi.org/10.1186/s12968-021-00824-2

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