@article {25224,
	title = {Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering applied to Healthy and Pathological Aortic Arches},
	journal = {IEEE Transactions on Biomedical Engineering},
	volume = {64},
	year = {2017},
	month = {2017},
	pages = {2373 - 2383},
	publisher = {ACM IEEE},
	keywords = {Medical diagnostic imaging, principal component analysis, Shape, Sociology, Three-dimensional displays},
	doi = {10.1109/TBME.2017.2655364},
	url = {http://ieeexplore.ieee.org/abstract/document/7857751/},
	author = {Jan L {Bruse} and Khushnood, Abbas and Kristin Sarah {Mcleod} and Hopewell N. {Ntsinjana} and Hsia, Tain-Yen and Sermesant, Maxime and Pennec, Xavier and Andrew M. {Taylor} and Schievano, Silvia}
}
@inproceedings {23830,
	title = {CMR-based 3D Statistical Shape Modelling Reveals Left Ventricular Morphological Differences Between Healthy Controls and Arterial Switch Operation Survivors},
	journal = {19th Annual Scientific Sessions of the Society for Cardiovascular Magnetic Resonance (SCMR)},
	year = {2016},
	note = {Walking Poster Session},
	month = {01/2016},
	publisher = {Simula},
	author = {Jan L {Bruse} and Hopewell N. {Ntsinjana} and Capelli, Claudio and Biglino, Giovanni and Kristin Sarah {Mcleod} and Sermesant, Maxime and Hsia, Tain-Yen and Schievano, Silvia and Andrew M. {Taylor}},
	editor = {Pennec, Xavier}
}
@article {24521,
	title = {A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: Assessing arch morphology of repaired coarctation of the aorta},
	journal = {BMC Medical Imaging},
	volume = {16},
	number = {1},
	year = {2016},
	month = {06/2016},
	pages = {40},
	publisher = {BMC},
	doi = {10.1186/s12880-016-0142-z},
	url = {http://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-016-0142-z},
	author = {Jan L {Bruse} and Kristin Sarah {Mcleod} and Biglino, Giovanni and Hopewell N. {Ntsinjana} and Capelli, Claudio and Hsia, Tain-Yen and Sermesant, Maxime and Pennec, Xavier and Andrew M. {Taylor} and Schievano, Silvia}
}
@inproceedings {23826,
	title = {A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?},
	journal = {Statistical Atlases and Computational Modeling of the Heart (STACOM 2015)},
	year = {2015},
	note = {Oral Presentation - Jan L. Bruse},
	month = {10/2015},
	publisher = {Lecture Notes in Computer Science, Springer. Verlag},
	edition = {MICCAI Workshop},
	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.\&nbsp;},
	keywords = {Aortic Arch, Coarctation of the Aorta, Mathematical Currents, Non-parametric Statistical Shape Model, Partial Least Square Regression},
	author = {Jan L {Bruse} and Kristin Sarah {Mcleod} and Biglino, Giovanni and Hopewell N. {Ntsinjana} and Capelli, Claudio and Hsia, Tain-Yen and Sermesant, Maxime and Pennec, Xavier and Taylor, Andrew and Schievano, Silvia}
}
