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Learning joint shape and appearance representations with metamorphic auto-encoders

Alexandre Bône 1, 2 Paul Vernhet 1, 2 Olivier Colliot 1, 2 Stanley Durrleman​ 1, 2
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : Transformation-based methods for shape analysis offer a consistent framework to model the geometrical content of images. Most often relying on diffeomorphic transforms, they lack however the ability to properly handle texture and differing topological content. Conversely, modern deep learning methods offer a very efficient way to analyze image textures. Building on the theory of metamorphoses, which models images as combined intensity-domain and spatial-domain transforms of a prototype, we introduce the "metamorphic" auto-encoding architecture. This class of neural networks is interpreted as a Bayesian generative and hierarchical model, allowing the joint estimation of the network parameters, a representative prototype of the training images, as well as the relative importance between the geometrical and texture contents. We give arguments for the practical relevance of the learned prototype and Euclidean latent-space metric, achieved thanks to an explicit normalization layer. Finally, the ability of the proposed architecture to learn joint and relevant shape and appearance representations from image collections is illustrated on BraTs 2018 datasets, showing in particular an encouraging step towards personalized numerical simulation of tumors with data-driven models.
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Contributor : Paul Vernhet Connect in order to contact the contributor
Submitted on : Tuesday, February 9, 2021 - 5:48:15 PM
Last modification on : Wednesday, January 26, 2022 - 2:32:04 PM
Long-term archiving on: : Monday, May 10, 2021 - 7:08:41 PM


  • HAL Id : hal-03136537, version 1


Alexandre Bône, Paul Vernhet, Olivier Colliot, Stanley Durrleman​. Learning joint shape and appearance representations with metamorphic auto-encoders. MICCAI 2020 - 23rd International Conference on Image Computing and Computer Assisted Interventions, Oct 2020, Lima / Virtual, Peru. ⟨hal-03136537⟩



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