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TVnet : Automated T...
TVnet : Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline
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- Gonzales, Ricardo A. (författare)
- 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,Universidad de Ingeniería y Tecnología (UTEC),Yale University,Skåne University Hospital
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- Lamy, Jérôme (författare)
- Yale University
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- Seemann, Felicia (författare)
- Lund University,Lunds universitet,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,Lund Cardiac MR Group,Lund University Research Groups,Yale University,Skåne University Hospital
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- Heiberg, Einar (författare)
- Lund University,Lunds universitet,Klinisk fysiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Hjärt-MR-gruppen i Lund,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,Clinical Physiology (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Lund Cardiac MR Group,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,Skåne University Hospital
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- Onofrey, John A. (författare)
- Yale University
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- Peters, Dana C. (författare)
- Yale University
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de Bruijne, Marleen (redaktör/utgivare)
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Cattin, Philippe C. (redaktör/utgivare)
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Cotin, Stéphane (redaktör/utgivare)
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Padoy, Nicolas (redaktör/utgivare)
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Speidel, Stefanie (redaktör/utgivare)
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Zheng, Yefeng (redaktör/utgivare)
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Essert, Caroline (redaktör/utgivare)
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(creator_code:org_t)
- 2021-09-21
- 2021
- Engelska 10 s.
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Ingår i: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030872304 ; 12906 LNCS, s. 567-576
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Tracking the tricuspid valve (TV) in magnetic resonance imaging (MRI) long-axis cine images has the potential to aid in the evaluation of right ventricular dysfunction, which is common in congenital heart disease and pulmonary hypertension. However, this annotation task remains difficult and time-demanding as the TV moves rapidly and is barely distinguishable from the myocardium. This study presents TVnet, a novel dual-stage deep learning pipeline based on ResNet-50 and automated image linear transformation, able to automatically derive tricuspid annular plane systolic excursion. Stage 1 uses a trained network for a coarse detection of the TV points, which are used by stage 2 to reorient the cine into a standardized size, cropping, resolution, and heart orientation and to accurately locate the TV points with another trained network. The model was trained and evaluated on 4170 images from 140 patients with diverse cardiovascular pathologies. A baseline model without standardization achieved a Euclidean distance error of 4.0 ± 3.1 mm and a clinical-metric agreement of ICC = 0.87, whereas a standardized model improved the agreement to 2.4 ± 1.7 mm and an ICC = 0.94, on par with an evaluated inter-observer variability of 2.9 ± 2.9 mm and an ICC = 0.92, respectively. This novel dual-stage deep learning pipeline substantially improved the annotation accuracy compared to a baseline model, paving the way towards reliable right ventricular dysfunction assessment with MRI.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- Annotation
- Cine MRI
- Residual neural networks
- Right ventricular dysfunction
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- Av författaren/redakt...
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Gonzales, Ricard ...
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Lamy, Jérôme
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Seemann, Felicia
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Heiberg, Einar
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Onofrey, John A.
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Peters, Dana C.
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visa fler...
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de Bruijne, Marl ...
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Cattin, Philippe ...
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Cotin, Stéphane
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Padoy, Nicolas
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Speidel, Stefani ...
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Zheng, Yefeng
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Essert, Caroline
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visa färre...
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- MEDICIN OCH HÄLSOVETENSKAP
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och Klinisk medicin
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och Kardiologi
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Medical Image Co ...
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Lunds universitet