Reference number : GC-2017-Q1-02643, amendment number : , agreement type : G, recipient legal name : Lombaert, Herve (École de technologie supérieure), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Context &ndash; This research program aims at exploring new directions for the analysis of shapes in medical images . The current challenge resides in the huge variability of complex biological shapes, such as the surface of the brain. Their complexity directly impacts the performance of learning algorithms in medical image analysis. This indicates a crucial need to better exploit the nature of shapes, particularly when data is analyzed on them. Shape analysis in medical imaging is today, often based on extrinsic geometric information, for instance, derived from Euclidean coordinates. As a result, traditional approaches inexorably require costly non-linear shape normalization, up to hours of computation for aligning shapes. On the other side, the intrinsic nature of shapes is often over simplified in medical image analysis, if not ignored. For instance, brain surfaces are typically treated as simple spheres in surface-based methods, increasing computational burden, or even ignored in volumetric methods, leading to misaligned surface data. This severely limits studies in medical imaging where data resides on complex surfaces. 
 
 Research Directions and Methodology &ndash; One promising avenue is to investigate shapes via spectral graph theory. This provides a foundation towards a truly intrinsic shape analysis, notably due to its invariance under isometry. The recent advances and the growing need to study surface data motivate the development of a new paradigm to perform statistics on complex biological shapes . To do so, I intend to develop three axes of research on spectral shape analysis: (i) shape representation , focused on harmonic shape modeling, (ii) shape statistics , focused on the learning of surface data, and (iii) shape dynamics , focused on motion of shapes. This program will first focus on the structural and functional variability of neuroimaging data, in order to discover the underlying mechanisms of neurodegenerative diseases. The long-term vision is to contribute towards a better use of medical data in learning algorithms by exploiting shape representations to detect biological abnormalities automatically. 
 
 Impact &ndash; Outcomes are expected to have a high direct impact in medical imaging, notably for studying functional data in the brain and cardiac imaging. The spectral framework provides a new paradigm to perform statistics on complex biological shapes. The computational advantage is expected to bring faster and more precise tools for studying functional data, notably in neuroimaging, which crucially needs a geometry-aware statistical framework. This brings, therefore, a strong advantage to lead future studies in functional neuroimaging with high potentials to significantly scale up future studies. The spectral framework is also relevant in various other fields where data fundamentally lives on surfaces, including in computer vision and machine learning., program purpose french : Context &ndash; This research program aims at exploring new directions for the analysis of shapes in medical images . The current challenge resides in the huge variability of complex biological shapes, such as the surface of the brain. Their complexity directly impacts the performance of learning algorithms in medical image analysis. This indicates a crucial need to better exploit the nature of shapes, particularly when data is analyzed on them. Shape analysis in medical imaging is today, often based on extrinsic geometric information, for instance, derived from Euclidean coordinates. As a result, traditional approaches inexorably require costly non-linear shape normalization, up to hours of computation for aligning shapes. On the other side, the intrinsic nature of shapes is often over simplified in medical image analysis, if not ignored. For instance, brain surfaces are typically treated as simple spheres in surface-based methods, increasing computational burden, or even ignored in volumetric methods, leading to misaligned surface data. This severely limits studies in medical imaging where data resides on complex surfaces. 
 
 Research Directions and Methodology &ndash; One promising avenue is to investigate shapes via spectral graph theory. This provides a foundation towards a truly intrinsic shape analysis, notably due to its invariance under isometry. The recent advances and the growing need to study surface data motivate the development of a new paradigm to perform statistics on complex biological shapes . To do so, I intend to develop three axes of research on spectral shape analysis: (i) shape representation , focused on harmonic shape modeling, (ii) shape statistics , focused on the learning of surface data, and (iii) shape dynamics , focused on motion of shapes. This program will first focus on the structural and functional variability of neuroimaging data, in order to discover the underlying mechanisms of neurodegenerative diseases. The long-term vision is to contribute towards a better use of medical data in learning algorithms by exploiting shape representations to detect biological abnormalities automatically. 
 
 Impact &ndash; Outcomes are expected to have a high direct impact in medical imaging, notably for studying functional data in the brain and cardiac imaging. The spectral framework provides a new paradigm to perform statistics on complex biological shapes. The computational advantage is expected to bring faster and more precise tools for studying functional data, notably in neuroimaging, which crucially needs a geometry-aware statistical framework. This brings, therefore, a strong advantage to lead future studies in functional neuroimaging with high potentials to significantly scale up future studies. The spectral framework is also relevant in various other fields where data fundamentally lives on surfaces, including in computer vision and machine learning., agreement title english : Spectral Shape Modeling for Medical Image Analysis, agreement title french : Spectral Shape Modeling for Medical Image Analysis, agreement number : RGPIN, agreement value : 145000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada