Spectral Shape Modeling for Medical Image Analysis

用于医学图像分析的光谱形状建模

• 批准号: RGPIN-2017-05420
• 负责人: Lombaert, Herve
• 金额: $ 2.11万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709847
• 关键词: Spectral; Shape; Modeling; Medical; Image; Spectral; Shape; Modeling; Medical; Image

Context 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 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 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.

背景此研究计划旨在探索新的方向，以分析医学图像中的形状。当前的挑战属于复杂生物形状的巨大变化，例如大脑的表面。它们的复杂性直接影响医学图像分析中学习算法的性能。这表明至关重要的需要更好地利用形状的性质，尤其是在对它们的数据进行分析时。如今，医学成像中的形状分析通常基于欧几里得坐标的外部几何信息。结果，传统方法不可避免地需要昂贵的非线性形状归一化，以数小时的计算来对齐形状。另一方面，如果不忽略，形状的内在性质通常会过于简化。例如，在基于表面的方法中，脑表面通常被视为简单球，增加了计算负担，甚至在体积方法中忽略了，导致表面数据不一致。这严重限制了数据驻留在复杂表面上的医学成像中。 研究方向和方法论一个有希望的途径是通过光谱图理论研究形状。这为真正的内在形状分析提供了基础，特别是由于其在等轴测图下的不变性。最近的进步以及研究表面数据的日益增长的需求激发了新范式的发展，以对复杂的生物形状进行统计。为此，我打算在频谱形状分析上开发三个研究轴：（i）形状表示，侧重于谐波形状建模，（ii）形状统计数据，专注于表面数据的学习以及（iii）形状动力学，集中于形状运动。该程序将首先关注神经影像数据的结构和功能变异性，以发现神经退行性疾病的潜在机制。长期的视觉是通过利用形状表示自动检测生物学异常来更好地利用医学数据在学习算法中。 预计影响结果将在医学成像中产生很大的直接影响，特别是用于研究大脑和心脏成像中的功能数据。光谱框架为进行复杂的生物形状执行统计提供了新的范式。预计计算优势将带来更快，更精确的工具来研究功能数据，尤其是在神经影像中，这至关重要的是需要几何学意识到的统计框架。因此，这为领导未来的研究具有高潜力的功能神经影像学研究带来了强大的优势，可以显着扩大未来的研究。光谱框架在其他各个领域也与数据从根本上生活在表面（包括计算机视觉和机器学习）上的各个领域相关。