Report
Wavelet-Based Multiscale Initial Flow For Improved Atlas Estimation in the Large Diffeomorphic Deformation Model Framework
| العنوان: | Wavelet-Based Multiscale Initial Flow For Improved Atlas Estimation in the Large Diffeomorphic Deformation Model Framework |
|---|---|
| المؤلفون: | Gaudfernau, Fleur, Blondiaux, Eléonore, Allassonnière, Stéphanie, Le Pennec, Erwan |
| المساهمون: | Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), CHU Trousseau APHP, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre de Mathématiques Appliquées de l'Ecole polytechnique (CMAP), Institut National de Recherche en Informatique et en Automatique (Inria)-École polytechnique (X), Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Centre National de la Recherche Scientifique (CNRS), This work was partly funded by the third author’s chair in the PRAIRIE institute funded by the French national agency ANR as part of the ”Investissements d’avenir” programme under the reference ANR-19- P3IA-0001., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019) |
| المصدر: | https://hal.science/hal-03620367 ; 2022. |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Deformable template model, Atlas estimation, Diffeomorphic deformations, Haar Wavelet, Coarse-to-fine algorithm, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing |
| الوصف: | Modelling the mean and variability in a population of images, a task referred to as atlas estimation, remains very challenging, especially in a clinical setting where deformations between images can occur at multiple scales. In this paper, we introduce a coarse-to-fine strategy for atlas estimation in the Large Deformation Diffeomorphic Metric Mapping framework, based on a finite parametrization of the subjects' velocity field. Using the Haar Wavelet Transform, a multiscale representation of the initial velocity fields is computed in order to optimize the template-to-subject deformations in a coarse-to-fine fashion. This reparametrization preserves the reproducing kernel Hilbert space structure of the velocity fields, enabling the algorithm to perform efficiently gradient descent. Numerical experiments on three different datasets, including a dataset of abnormal fetal brain images, show that compared to the original algorithm, the coarse-to-fine strategy reaches higher performance and yields template images that preserve important details while avoiding unrealistic features. |
| نوع الوثيقة: | report |
| اللغة: | English |
| الاتاحة: | https://hal.science/hal-03620367 https://hal.science/hal-03620367v1/document https://hal.science/hal-03620367v1/file/Gaudfernau_2022_IJCV_CTF.pdf |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.F3A7B371 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |