1 code implementation • 15 Jan 2025 • Huiyu Li, Nicholas Ayache, Hervé Delingette
In this paper, we address the medical image anonymization problem with a two-stage solution: latent code projection and optimization.
no code implementations • 29 Jan 2024 • Wilhelm Wimmer, Hervé Delingette
The application of kinematic surface fitting, a method for characterizing shapes through parametric stationary velocity fields, has shown promising results in computer vision and computer-aided design.
no code implementations • 14 Sep 2023 • Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna, Nicholas Ayache, Hervé Delingette
We compared extensively our method on several datasets with the STAPLE method and the naive segmentation averaging method, showing that it leads to binary consensus masks of intermediate size between Majority Voting and STAPLE and to different posterior probabilities than Mask Averaging and STAPLE methods.
no code implementations • 31 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.
no code implementations • 7 Jun 2022 • Huiyu Li, Nicholas Ayache, Hervé Delingette
Instead, only a secured remote access to a data lake is granted to the model owner without any ability to retrieve data from the data lake.
no code implementations • 9 Apr 2022 • Nathan Blanken, Jelmer M. Wolterink, Hervé Delingette, Christoph Brune, Michel Versluis, Guillaume Lajoinie
The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.
no code implementations • 22 Dec 2021 • Florent Jousse, Xavier Pennec, Hervé Delingette, Matilde Gonzalez
This work addresses the problem of non-rigid registration of 3D scans, which is at the core of shape modeling techniques.
no code implementations • MICCAI Workshop COMPAY 2021 • Paul Tourniaire, Marius Ilie, Paul Hofman, Nicholas Ayache, Hervé Delingette
Since the standardization of Whole Slide Images (WSIs) digitization, the use of deep learning methods for the analysis of histological images has shown much potential.
1 code implementation • 5 May 2021 • ZiHao Wang, Hervé Delingette
To further advance learning approaches in image registration, we introduce an attention mechanism in the deformable image registration problem.
no code implementations • 3 Nov 2020 • Julian Krebs, Hervé Delingette, Nicholas Ayache, Tommaso Mansi
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration.
no code implementations • 27 Oct 2020 • ZiHao Wang, Zhifei Xu, Jiayi He, Chulsoon Hwang, Jun Fan, Hervé Delingette
In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation.
no code implementations • 2 Sep 2020 • Zihao Wang, Hervé Delingette
The HVAE adapted the Hamiltonian dynamic flow into variational inference that significantly improves the performance of the posterior estimation.
no code implementations • MIDL 2019 • Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, Nicolas Guevara, Hervé Delingette
Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning.
no code implementations • 31 Jul 2019 • Julian Krebs, Tommaso Mansi, Nicholas Ayache, Hervé Delingette
We propose to learn a probabilistic motion model from a sequence of images.
no code implementations • 3 Jul 2019 • Pawel Mlynarski, Hervé Delingette, Hamza Alghamdi, Pierre-Yves Bondiau, Nicholas Ayache
We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered organs at risk, which were originally used for radiotherapy planning.
no code implementations • 3 Jul 2019 • Wilhelm Wimmer, Clair Vandersteen, Nicolas Guevara, Marco Caversaccio, Hervé Delingette
Herein, we present an algorithm for robust modiolar axis detection in clinical CT images.
no code implementations • 23 May 2019 • Raphaël Sivera, Hervé Delingette, Marco Lorenzi, Xavier Pennec, Nicholas Ayache
In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution.
no code implementations • 15 Feb 2019 • Qiao Zheng, Hervé Delingette, Kenneth Fung, Steffen E. Petersen, Nicholas Ayache
First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion.
no code implementations • 18 Dec 2018 • Julian Krebs, Hervé Delingette, Boris Mailhé, Nicholas Ayache, Tommaso Mansi
Besides, we visualized the learned latent space and show that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.
Ranked #1 on
Diffeomorphic Medical Image Registration
on Automatic Cardiac Diagnosis Challenge (ACDC)
(using extra training data)
Deformable Medical Image Registration
Diffeomorphic Medical Image Registration
+2
no code implementations • 10 Dec 2018 • Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache
In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation.
no code implementations • 6 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.
1 code implementation • 8 Nov 2018 • Qiao Zheng, Hervé Delingette, Nicholas Ayache
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart.
no code implementations • 23 Jul 2018 • Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache
Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences.
1 code implementation • 25 Apr 2018 • Qiao Zheng, Hervé Delingette, Nicolas Duchateau, Nicholas Ayache
We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex).
no code implementations • 19 Apr 2018 • Julian Krebs, Tommaso Mansi, Boris Mailhé, Nicholas Ayache, Hervé Delingette
This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space.
no code implementations • 29 Mar 2018 • Qiao Zheng, Hervé Delingette, Nicolas Duchateau, Nicholas Ayache
We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes.