کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4953333 | 1443006 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms
ترجمه فارسی عنوان
مدل سازی احتمالی تغییرات آناتومیکی با استفاده از پارامترهای کم ابعاد مختصات
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
گرافیک کامپیوتری و طراحی به کمک کامپیوتر
چکیده انگلیسی
We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCAâ¯(TPCA) and probabilistic principal geodesic analysisâ¯(PPGA) that operate in the high dimensional image space.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Medical Image Analysis - Volume 41, October 2017, Pages 55-62
Journal: Medical Image Analysis - Volume 41, October 2017, Pages 55-62
نویسندگان
Miaomiao Zhang, William M. III, Polina Golland,