Search Results for author: Maxime Sermesant

Found 7 papers, 0 papers with code

Zero-shot-Learning Cross-Modality Data Translation Through Mutual Information Guided Stochastic Diffusion

no code implementations31 Jan 2023 ZiHao Wang, Yingyu Yang, Maxime Sermesant, Hervé Delingette, Ona Wu

This paper proposes a new unsupervised zero-shot-learning method named Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion), which learns to translate the unseen source data to the target domain.

Denoising Translation +1

Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers

no code implementations21 Nov 2022 ZiHao Wang, Yingyu Yang, Maxime Sermesant, Herve Delingette

In the meantime, recent studies show that the attention-based model (e. g., Transformer) can bring superior performance in pattern recognition tasks.

Image Registration

Cardiac Motion Modeling with Parallel Transport and Shape Splines

no code implementations17 Feb 2021 Nicolas Guigui, Pamela Moceri, Maxime Sermesant, Xavier Pennec

In cases of pressure or volume overload, probing cardiac function may be difficult because of the interactions between shape and deformations. In this work, we use the LDDMM framework and parallel transport to estimate and reorient deformations of the right ventricle.

Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets

no code implementations2 Oct 2020 Jaume Banus, Maxime Sermesant, Oscar Camara, Marco Lorenzi

To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data.

Anatomy Imputation

Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss

no code implementations6 Dec 2018 Shuman Jia, Antoine Despinasse, ZiHao Wang, Hervé Delingette, Xavier Pennec, Pierre Jaïs, Hubert Cochet, Maxime Sermesant

In this preliminary study, we propose automated, two-stage, three-dimensional U-Nets with convolutional neural network, for the challenging task of left atrial segmentation.

Anatomy Image Segmentation +3

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