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                        human heart from 2D cine magnetic resonance slices.
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                           schemename="dnet:dataCite_title">A completely automated pipeline for 3D reconstruction of
                        human heart from 2D cine magnetic resonance slices
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                    <creator rank="8" URL="https://academic.microsoft.com/#/detail/2103042895">Vicente Grau</creator>
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                             URL="https://academic.microsoft.com/#/detail/2693349397">Abhirup Banerjee
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                    <creator rank="3" URL="https://academic.microsoft.com/#/detail/1425738153">Ernesto Zacur</creator>
                    <creator rank="6" URL="https://academic.microsoft.com/#/detail/2043191934">Robin P. Choudhury
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                    <creator rank="7" URL="https://academic.microsoft.com/#/detail/2166186014">Blanca Rodriguez
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                    <creator rank="5" URL="https://academic.microsoft.com/#/detail/264390263">Yoram Rudy</creator>
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                    <description>Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and
                        characterization of cardiovascular diseases, since it can identify abnormalities in structure
                        and function of the myocardium non-invasively and without the need for ionizing radiation.
                        However, in clinical practice, it is commonly acquired as a collection of separated and
                        independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a
                        completely automated pipeline for generating patient-specific 3D biventricular heart models from
                        cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR
                        images, segments them using a deep learning-based method to extract the heart contours, and
                        aligns the contours in 3D space correcting possible misalignments due to breathing or subject
                        motion first using the intensity and contours information from the cine data and next with the
                        help of a statistical shape model. Finally, the sparse 3D representation of the contours is used
                        to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated
                        in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment
                        artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the
                        final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our
                        computational pipeline are used for simulations of electrical activation patterns, showing
                        agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented
                        here for patient-specific MR imaging-based 3D biventricular representations contribute to the
                        efficient realization of precision medicine, enabling the enhanced interpretability of clinical
                        data, the digital twin vision through patient-specific image-based modelling and simulation, and
                        augmented reality applications.
                        This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new
                        challenges and opportunities’.
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                             schemeid="dnet:subject_classification_typologies"
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                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies">Cine mri
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                    <subject classid="MAG" classname="Microsoft Academic Graph classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies">Structure and function
                    </subject>
                    <subject classid="MAG" classname="Microsoft Academic Graph classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies">Cardiac magnetic resonance
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                             schemename="dnet:subject_classification_typologies">Magnetic resonance imaging
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                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:actionset" trust="0.5338496">medicine.diagnostic_test
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                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:actionset" trust="0.5338496">medicine
                    </subject>
                    <subject classid="MAG" classname="Microsoft Academic Graph classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies">Human heart
                    </subject>
                    <subject classid="MAG" classname="Microsoft Academic Graph classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies">Modality (human–computer interaction)
                    </subject>
                    <subject classid="MAG" classname="Microsoft Academic Graph classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies">3D reconstruction
                    </subject>
                    <subject classid="MAG" classname="Microsoft Academic Graph classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies">Computer science
                    </subject>
                    <subject classid="MAG" classname="Microsoft Academic Graph classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies">Nuclear magnetic resonance
                    </subject>
                    <subject classid="SDG" classname="Sustainable Development Goals"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:sdg">3. Good health
                    </subject>
                    <subject classid="FOS" classname="Fields of Science and Technology classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">03 medical and health sciences
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                    <subject classid="FOS" classname="Fields of Science and Technology classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">0302 clinical medicine
                    </subject>
                    <subject classid="FOS" classname="Fields of Science and Technology classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">030218 Nuclear Medicine &amp;
                        Medical Imaging
                    </subject>
                    <subject classid="FOS" classname="Fields of Science and Technology classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">03021801 Radiology/Image
                        segmentation
                    </subject>
                    <subject classid="FOS" classname="Fields of Science and Technology classification"
                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">03021801 Radiology/Image
                        segmentation - deep learning/datum
                    </subject>
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                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">030204 Cardiovascular System
                        &amp; Hematology
                    </subject>
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                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">03020401 Aging-associated
                        diseases/Heart diseases
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                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">030217 Neurology &amp;
                        Neurosurgery
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                             schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="true"
                             inferenceprovenance="update" provenanceaction="subject:fos">03021701 Brain/Neural circuits
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                             provenanceaction="sysimport:crosswalk:repository" trust="0.9">Articles
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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                             provenanceaction="sysimport:crosswalk:repository" trust="0.9">Research Articles
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:crosswalk:repository" trust="0.9">cardiac mesh reconstruction
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:crosswalk:repository" trust="0.9">cine MRI
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:crosswalk:repository" trust="0.9">misalignment correction
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:crosswalk:repository" trust="0.9">electrophysiological
                        simulation
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:crosswalk:repository" trust="0.9">ECGI
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:actionset" trust="0.9">Heart
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:actionset" trust="0.9">Humans
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:actionset" trust="0.9">Imaging, Three-Dimensional
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Imaging
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Imaging, Cine
                    </subject>
                    <subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
                             schemename="dnet:subject_classification_typologies" inferred="false"
                             provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Spectroscopy
                    </subject>
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                                  provenanceaction="sysimport:crosswalk:repository" trust="0.9">2021-01-01
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                                  provenanceaction="sysimport:crosswalk:repository" trust="0.9">2023-01-05
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                                  schemename="dnet:dataCite_date" inferred="false"
                                  provenanceaction="sysimport:crosswalk:repository" trust="0.9">2023-01-05
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                    <publisher>The Royal Society</publisher>
                    <source>Crossref</source>
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                    <source>Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
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                    <journal issn="1364-503X" eissn="1471-2962" vol="379">Philosophical Transactions of the Royal
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                    </journal>
                    <context id="dth" label="Digital Twins in Health" type="community"></context>
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                            <legalname>University of Oxford</legalname>
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                                 trust="0.9">10.6084/m9.figshare.15656924.v2
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                            <dateofacceptance>2021-01-01</dateofacceptance>
                            <title classid="main title" classname="main title" schemeid="dnet:dataCite_title"
                                   schemename="dnet:dataCite_title">Implementation Details of the Reconstruction
                                Pipeline and Electrophysiological Inference Results from A completely automated pipeline
                                for 3D reconstruction of human heart from 2D cine magnetic resonance slices
                            </title>
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                            <acronym>CompBioMed2</acronym>
                            <title>A Centre of Excellence in Computational Biomedicine</title>
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                            <dateofacceptance>2021-01-01</dateofacceptance>
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                            <publisher>The Royal Society</publisher>
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                                   schemename="dnet:dataCite_title">Montage Video of the Stepwise Performance of 3D
                                Reconstruction Pipeline on All 20 Patients from A completely automated pipeline for 3D
                                reconstruction of human heart from 2D cine magnetic resonance slices
                            </title>
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                            <dateofacceptance>2021-01-01</dateofacceptance>
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                            <title classid="main title" classname="main title" schemeid="dnet:dataCite_title"
                                   schemename="dnet:dataCite_title">Implementation Details of the Reconstruction
                                Pipeline and Electrophysiological Inference Results from A completely automated pipeline
                                for 3D reconstruction of human heart from 2D cine magnetic resonance slices
                            </title>
                            <publisher>The Royal Society</publisher>
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