4 citations · 9 across the 6 of their papers we have counts for
11 papers · 1 filter
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…