title: A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta.
abstract: Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements.
  
  
  
  Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters.
  
  
  
  The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors.
  
  
  
  The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.
authors.affiliation: Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK. jan.bruse.12@ucl.ac.uk.
authors.forename: Jan L
authors.initials: JL
authors.lastname: Bruse
authors.affiliation: Cardiac Modelling Department, Simula Research Laboratory, Oslo, Norway.|Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France.
authors.forename: Kristin
authors.initials: K
authors.lastname: McLeod
authors.affiliation: Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK.|Bristol Heart Institute, School of Clinical Sciences, University of Bristol, Bristol, UK.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Biglino
authors.affiliation: Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK.
authors.forename: Hopewell N
authors.initials: HN
authors.lastname: Ntsinjana
authors.affiliation: Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK.
authors.forename: Claudio
authors.initials: C
authors.lastname: Capelli
authors.affiliation: Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK.
authors.forename: Tain-Yen
authors.initials: TY
authors.lastname: Hsia
authors.affiliation: Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France.
authors.forename: Maxime
authors.initials: M
authors.lastname: Sermesant
authors.affiliation: Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France.
authors.forename: Xavier
authors.initials: X
authors.lastname: Pennec
authors.affiliation: Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Taylor
authors.affiliation: Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK.
authors.forename: Silvia
authors.initials: S
authors.lastname: Schievano
mesh_terms: D001013:Aorta, Thoracic / Q000000981:diagnostic imaging*; D001017:Aortic Coarctation / Q000000981:diagnostic imaging* / Q000503:physiopathology / Q000628:therapy*; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008953:Models, Anatomic; D015233:Models, Statistical; D013318:Stroke Volume; D016896:Treatment Outcome
keywords: 3D Shape analysis; Coarctation of the aorta; Computational modelling; Congenital heart disease; Statistical shape model (SSM)