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Learning Shape Priors from Pieces

Madsen, Dennis and Aellen, Jonathan and Morel-Forster, Andreas and Vetter, Thomas and Lüthi, Marcel. (2020) Learning Shape Priors from Pieces. In: Shape in Medical Imaging. Cham, pp. 30-43.

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Official URL: https://edoc.unibas.ch/81303/

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Abstract

Point Distribution Models (PDM) require a dataset in which point-to-point correspondence between the individual shapes has been established. However, in the medical domain, minimising radiation exposure and pathological deformations are reasons why healthy anatomies are often only available as partial observations. To exploit the partial shapes for learning shape models, previous methods required at least a few complete shapes and, either a robust registration method or a robust learning algorithm. Our proposed method implements the idea of multiple imputations from Bayesian statistics. We learn a PDM from a dataset consisting of only incomplete shapes and a single full template. For this, we first estimate the posterior distribution of point-to-point registrations for each partial observation. Then we construct the PDM from the set of registration distributions. We quantitatively evaluate our method on a 2D dataset of hands and a 3D dataset of femurs with known ground-truth. Furthermore, we showcase how to use our method on only partial clinical data to build a 3D statistical model of the human skull. The code is made open-source and the synthetic dataset publicly available.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Computergraphik Bilderkennung (Vetter)
UniBasel Contributors:Vetter, Thomas and Aellen, Jonathan and Morel, Andreas and Lüthi, Marcel
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:Springer
ISBN:978-3-030-61055-5
e-ISBN:978-3-030-61056-2
Series Name:Lecture Notes in Computer Science
Issue Number:12474
Note:Publication type according to Uni Basel Research Database: Conference paper
Identification Number:
Last Modified:27 Jan 2021 08:49
Deposited On:27 Jan 2021 08:49

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