@misc{13427,
  keywords = {Non-parametric Statistical Shape Model, Mathematical Currents, Partial Least Square Regression, Coarctation of the Aorta, Aortic Arch},
  author = {Jan Bruse and Kristin Mcleod and Giovanni Biglino and Hopewell Ntsinjana and Claudio Capelli and Tain-Yen Hsia and Maxime Sermesant and Xavier Pennec and Andrew Taylor and Silvia Schievano},
  title = {A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?},
  abstract = {Coarctation of the Aorta (CoA) is a cardiac defect that re- quires surgical intervention aiming to restore an unobstructed aortic arch shape. Many patients suffer from complications post-repair, which are commonly associated with arch shape abnormalities. Determining the degree of shape abnormality could improve risk stratification in recom- mended screening procedures. Yet, traditional morphometry struggles to capture the highly complex arch geometries. Therefore, we use a non- parametric Statistical Shape Model based on mathematical currents to fully account for 3D global and regional shape features. By comput- ing a template aorta of a population of healthy subjects and analysing its transformations towards CoA arch shape models using Partial Least Squares regression techniques, we derived a shape vector as a measure of subject-specific shape abnormality. Results were compared to a shape ranking by clinical experts. Our study suggests Statistical Shape Mod- elling to be a promising diagnostic tool for improved screening of complex cardiac defects.},
  year = {2015},
  journal = {Statistical Atlases and Computational Modeling of the Heart (STACOM 2015)},
  edition = {MICCAI Workshop},
  month = {10/2015},
  publisher = {Lecture Notes in Computer Science, Springer. Verlag},
  note = {Oral Presentation - Jan L. Bruse},
}