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Zhao D, Ferdian E, Maso Talou GD, Gilbert K, Quill GM, Wang VY, Pedrosa J, D'hooge J, Sutton T, Lowe BS, Legget ME, Ruygrok PN, Doughty RN, Young AA, Nash MP. Leveraging CMR for 3D echocardiography: an annotated multimodality dataset for AI. Eur Heart J Cardiovasc Imaging 2022. [DOI: 10.1093/ehjci/jeac141.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Funding Acknowledgements: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council of New Zealand (HRC)
National Heart Foundation of New Zealand (NHF)
Segmentation of the left ventricular myocardium and cavity in 3D echocardiography (3DE) is a critical task for the quantification of systolic function in heart disease. Continuing advances in 3DE have considerably improved image quality, prompting increased clinical uptake in recent years, particularly for volumetric measurements. Nevertheless, analysis of 3DE remains a difficult problem due to inherently complex noise characteristics, anisotropic image resolution, and regions of acoustic dropout.
One of the primary challenges associated with the development of automated methods for 3DE analysis is the requirement of a sufficiently large training dataset. Historically, ground truth annotations have been difficult to obtain due to the high degree of inter- and intra-observer variability associated with manual 3DE segmentation, thus, limiting the scope of AI-based solutions. To address the lack of expert consensus, we instead used labels derived from cardiac magnetic resonance (CMR) images of the same subjects. By spatiotemporally registering CMR labels to corresponding 3DE image data on a per subject basis (Figure 1), we collated 520 annotated 3DE images from a mixed cohort of 130 human subjects (2 independent single-beat acquisitions per subject at end-diastole and end-systole) consisting of healthy controls and patients with acquired cardiac disease. Comprising images acquired across a range of patient demographics, this curated dataset exhibits variation in image quality, 3DE acquisition parameters, as well as left ventricular shape and pose within the 3D image volume.
To demonstrate the utility of such a dataset, nn-UNet, a self-configuring deep learning method for semantic segmentation was employed. An 80/20 split of the dataset was used for training and testing, respectively, and data augmentations were applied in the form of scaling, rotation, and reflection. The trained network was capable of reproducing measurements derived from CMR for end-diastolic volume, end-systolic volume, ejection fraction, and mass, while outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility (Table I).
As part of ongoing efforts to improve the accuracy and efficiency of 3DE analysis, we have leveraged the high resolution and signal-to-noise-ratio of CMR (relative to 3DE), to create a novel, publicly available benchmark dataset for developing and evaluating 3DE labelling methods. This approach not only significantly reduces the effects of observer-specific bias and variability in training data arising from conventional manual 3DE analysis methods, but also improves the agreement between cardiac indices derived from 3DE and CMR.
Figure 1. Data annotation workflow Table I. Results
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Affiliation(s)
- D Zhao
- The University of Auckland, Auckland Bioengineering Institute , Auckland , New Zealand
| | - E Ferdian
- The University of Auckland, Department of Anatomy and Medical Imaging , Auckland , New Zealand
| | - G D Maso Talou
- The University of Auckland, Auckland Bioengineering Institute , Auckland , New Zealand
| | - K Gilbert
- The University of Auckland, Auckland Bioengineering Institute , Auckland , New Zealand
| | - G M Quill
- The University of Auckland, Auckland Bioengineering Institute , Auckland , New Zealand
| | - V Y Wang
- The University of Auckland, Auckland Bioengineering Institute , Auckland , New Zealand
| | - J Pedrosa
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) , Porto , Portugal
| | - J D'hooge
- KU Leuven, Department of Cardiovascular Sciences , Leuven , Belgium
| | - T Sutton
- Counties Manukau Health Cardiology , Auckland , New Zealand
| | - B S Lowe
- Auckland City Hospital, Green Lane Cardiovascular Service , Auckland , New Zealand
| | - M E Legget
- The University of Auckland, Department of Medicine , Auckland , New Zealand
| | - P N Ruygrok
- The University of Auckland, Department of Medicine , Auckland , New Zealand
| | - R N Doughty
- The University of Auckland, Department of Medicine , Auckland , New Zealand
| | - A A Young
- King's College London, Department of Biomedical Engineering , London , United Kingdom of Great Britain & Northern Ireland
| | - M P Nash
- The University of Auckland, Auckland Bioengineering Institute , Auckland , New Zealand
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Cury LFM, Maso Talou GD, Younes-Ibrahim M, Blanco PJ. Parallel generation of extensive vascular networks with application to an archetypal human kidney model. R Soc Open Sci 2021; 8:210973. [PMID: 34966553 PMCID: PMC8633801 DOI: 10.1098/rsos.210973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 10/28/2021] [Indexed: 05/25/2023]
Abstract
Given the relevance of the inextricable coupling between microcirculation and physiology, and the relation to organ function and disease progression, the construction of synthetic vascular networks for mathematical modelling and computer simulation is becoming an increasingly broad field of research. Building vascular networks that mimic in vivo morphometry is feasible through algorithms such as constrained constructive optimization (CCO) and variations. Nevertheless, these methods are limited by the maximum number of vessels to be generated due to the whole network update required at each vessel addition. In this work, we propose a CCO-based approach endowed with a domain decomposition strategy to concurrently create vascular networks. The performance of this approach is evaluated by analysing the agreement with the sequentially generated networks and studying the scalability when building vascular networks up to 200 000 vascular segments. Finally, we apply our method to vascularize a highly complex geometry corresponding to the cortex of a prototypical human kidney. The technique presented in this work enables the automatic generation of extensive vascular networks, removing the limitation from previous works. Thus, we can extend vascular networks (e.g. obtained from medical images) to pre-arteriolar level, yielding patient-specific whole-organ vascular models with an unprecedented level of detail.
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Affiliation(s)
- L. F. M. Cury
- National Laboratory for Scientific Computing, LNCC/MCTI, Petrópolis, Brazil
- National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, Brazil
| | - G. D. Maso Talou
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - M. Younes-Ibrahim
- Faculty of Medical Sciences, Rio de Janeiro State University, UERJ, Rio de Janeiro, Brazil
- National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, Brazil
| | - P. J. Blanco
- National Laboratory for Scientific Computing, LNCC/MCTI, Petrópolis, Brazil
- National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, Brazil
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Blanco PJ, Bulant CA, Müller LO, Talou GDM, Bezerra CG, Lemos PA, Feijóo RA. Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve. Sci Rep 2018; 8:17275. [PMID: 30467321 PMCID: PMC6250665 DOI: 10.1038/s41598-018-35344-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 11/01/2018] [Indexed: 02/05/2023] Open
Abstract
In this work we propose to validate the predictive capabilities of one-dimensional (1D) blood flow models with full three-dimensional (3D) models in the context of patient-specific coronary hemodynamics in hyperemic conditions. Such conditions mimic the state of coronary circulation during the acquisition of the Fractional Flow Reserve (FFR) index. Demonstrating that 1D models accurately reproduce FFR estimates obtained with 3D models has implications in the approach to computationally estimate FFR. To this end, a sample of 20 patients was employed from which 29 3D geometries of arterial trees were constructed, 9 obtained from coronary computed tomography angiography (CCTA) and 20 from intra-vascular ultrasound (IVUS). For each 3D arterial model, a 1D counterpart was generated. The same outflow and inlet pressure boundary conditions were applied to both (3D and 1D) models. In the 1D setting, pressure losses at stenoses and bifurcations were accounted for through specific lumped models. Comparisons between 1D models (FFR1D) and 3D models (FFR3D) were performed in terms of predicted FFR value. Compared to FFR3D, FFR1D resulted with a difference of 0.00 ± 0.03 and overall predictive capability AUC, Acc, Spe, Sen, PPV and NPV of 0.97, 0.98, 0.90, 0.99, 0.82, and 0.99, with an FFR threshold of 0.8. We conclude that inexpensive FFR1D simulations can be reliably used as a surrogate of demanding FFR3D computations.
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Affiliation(s)
- P J Blanco
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil.
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil.
| | - C A Bulant
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - L O Müller
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - G D Maso Talou
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - C Guedes Bezerra
- Department of Interventional Cardiology, Heart Institute (InCor) and the University of São Paulo Medical School, Sao Paulo, SP, 05403-904, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - P A Lemos
- Department of Interventional Cardiology, Heart Institute (InCor) and the University of São Paulo Medical School, Sao Paulo, SP, 05403-904, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - R A Feijóo
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
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