250 citations · 531 across the 23 of their papers we have counts for
13 papers · 1 filter
Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)
Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna +2
The extraction of consensus segmentations from several binary or probabilistic masks is important to solve various tasks such as the analysis of inter-rater variability or the fusi…
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix
Julian Krebs, Hervé Delingette, Nicholas Ayache +1
We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the…
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI
Julian Krebs, Tommaso Mansi, Nicholas Ayache +1
We propose to learn a probabilistic motion model from a sequence of images. Besides spatio-temporal registration, our method offers to predict motion from a limited number of frame…
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
Qiao Zheng, Hervé Delingette, Kenneth Fung +2
We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previousl…
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
Pawel Mlynarski, Hervé Delingette, Antonio Criminisi +1
Most of the current state-of-the-art methods for tumor segmentation are based on machine learning models trained on manually segmented images. This type of training data is particu…
Learning a Probabilistic Model for Diffeomorphic Registration
Julian Krebs, Hervé Delingette, Boris Mailhé +2
We propose to learn a low-dimensional probabilistic deformation model from data which can be used for registration and the analysis of deformations. The latent variable model maps…
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
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. An original semi-su…
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
Pawel Mlynarski, Hervé Delingette, Antonio Criminisi +1
We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have…
3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation
Qiao Zheng, Hervé Delingette, Nicolas Duchateau +1
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 (arou…
Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training
Wen Wei, Emilie Poirion, Benedetta Bodini +4
Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS). A reliable measure of the tissue myelin content is therefore essential for the understanding…
Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
Julian Krebs, Tommaso Mansi, Boris Mailhé +2
We propose a deformable registration algorithm based on unsupervised learning of a low-dimensional probabilistic parameterization of deformations. We model registration in a probab…
3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
Qiao Zheng, Hervé Delingette, Nicolas Duchateau +1
We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it…
Automatic View-Point Selection for Inter-Operative Endoscopic Surveillance
Anant S. Vemuri, Stephane A. Nicolau, Jacques Marescaux +2
Esophageal adenocarcinoma arises from Barrett's esophagus, which is the most serious complication of gastroesophageal reflux disease. Strategies for screening involve periodic surv…